Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce

Abstract

Integrating Artificial Intelligence (AI) in agriculture marks a new era of precision and efficiency. Convolutional Neural Networks (CNNs) enable early crop disease detection through image-based classification, reducing yield loss. Long Short-Term Memory (LSTM) networks support predictive modelling for yield forecasting and soil health assessment, aiding resource allocation. While mechanization and automation remain global challenges, modern AI and machine learning (ML) applications have transformed agricultural practices. This review explores various AI tools, including ML algorithms, deep learning (DL) models, Internet of Things (IoT), and Decision Support Systems (DSS), and their role in addressing challenges like maximizing crop yield, precision irrigation, pest control, and informed decision-making. The paper further highlights AI applications in plant breeding, irrigation, logistics, and packaging. Despite the advancements, widespread adoption faces barriers such as high costs, privacy concerns, inadequate infrastructure, and limited technical knowledge. The review offers insights into both the potential and limitations of AI in agriculture.

Highlights

• •Focuses on the AI based technologies currently being used in agriculture.
• •Emphasis AI powered technologies used for improving productivity and sustainability.
• •Presents an overview on the challenges in the field of AI and food sector.
• •Assess smart irrigation and sensor-based monitoring for improving agriculture practices.

1. Introduction

The agriculture sector contributes to the viable economy of any country. In recent years, revolutionary transformation has been seen in the agricultural industry due to the rapid advancement and application of the AI concept. The areas of agriculture that the use of AI can completely transform are precision farming and supply chain management (Arjun, 2013). For example, Unmanned Aerial Systems (UAS) are being widely used in various applications such as disease detection, yield estimation, growth prediction, and weed management. Soil monitoring and precision irrigation are now being transformed by the use of IoT sensors and AI algorithms, which enable the maximization of resources (Tsouros et al., 2019). Additionally, for harvesting, sorting, and grading, AI-powered robots are widely adopted so that the tasks are performed with greater efficiency and accuracy. On the other hand, DL-based models and remote sensing are being used for crop identification, land cover estimation, and moisture detection (Kamilaris et al., 2018). A noteworthy contribution has been made by AI in improving the engineering properties, such as boiling point, density, viscosity, and mass of different agricultural produce. It is very crucial to improve the engineering properties as they have a significant impact on various parameters such as quality, safety, and sales of the products (Khan, Kumar, Dhingra, & Bhati, 2021). Usually, these attributes are assessed and optimized manually, but there are certain drawbacks, such as a labor-intensive process, more time-consuming, and higher error chances. Thus, the use of AI has resulted in quick, easy, and accurate assessment. The adoption of AI-based technologies for solving problems has progressively made the agriculture sector more sustainable and efficient (Ben-Ayed et al., 2013).

A ML approach using ANN and SVM was introduced by Yuze et al., 2022 for predicting the boiling point of organic molecules. The model based on this approach showed high accuracy among a test sample containing 4550 compounds. Similarly, a ML-based predictive model was developed by Panwar et al., 2023 for predicting the density and viscosity of 305 complex carbohydrates. The model shows high accuracy with a coefficient of determination (R (Abedin et al., 2017)) being 99.6 % and 97.9 % for density and viscosity, respectively. A study conducted by Chasiotis et al., 2020 used a multilayer perceptron (MLP) ANN model for moisture content analysis of quince slices. They have about 81 % moisture content on a wet basis. The drying process was carried out using a fluidized bed dryer. R² > 0.99 shows very high accuracy rates for moisture content prediction and low error. Similarly, another predictive model was developed by Fajardo Muñoz et al., 2023 using MLP-ANN for determining the yield of essential oil from orange-peel mass through steam distillation. A R² value of 97.6 % shows high prediction accuracy. Another study was done to model the variation in the moisture content of mushrooms using ANN during drying. The dynamic ANN structure with 3 inputs, 1 output, and 2 hidden layers showed the best performance. R value of 0.9914 showed high accuracy and energy efficiency.

In the field of precision farming, IoT may be defined as an interconnected network of devices that collect data with the help of sensors, drones, GPS monitors, and exchange the collected information to support decision making as well as automation. According to Verdouw et al., 2016 IoT is the foundation of AI integration for precision farming as it supports the controlling, monitoring, and optimization of agricultural practices. Discussing big data in smart farming, it was quoted by Wolfert et al., 2017 that IoT is a crucial component that supports data-driven decision making. Similarly, crop models are an essential tool that supports the evaluation of plant growth under different conditions. By the integration of crop models with AI, the predictive power is greatly enhanced as they turn the raw data obtained from sensors into usable information based on predictions of how crops are growing under different conditions (Chlingaryan et al., 2018). Decision Support System (DSS) serves as a bridge between AI models and the practical farm approach. DSS may be defined as a computer tool that helps in making decisions by collecting, processing, and analysing data to provide useful information. One such example is the Decision Support System for Agro-Technology Transfer Model (DSSAT), which is a tool that provides information on soil, weather, details of crop, and other parameters to know how the crop will grow and provide recommendations on how to improve the yields. Hence supporting better decision making (Mulla et al., 2020). On the other hand, Asolo et al., 2024 talk about a DSS that uses a chatbot to help farmers. This system works by providing up-to-date guidance on the most efficient methods for managing farms and growing crops by using ML and data analysis. The chatbot answers all the questions farmers ask and provides them with suitable suggestions. Another example discussed by Kukar et al., 2019 is AgroDSS may be defined as a cloud-based DSS that focuses on assisting farmers in precision agriculture. This system works by integrating the current farm management information system and then providing a DSS. With the help of this, farmers upload the data on their own, use different analysis methods for the data, and finally retrieve the new updated data based on the findings. The system includes features like accuracy checking, making predictions, and identifying important changes. Another example is a semi-automated pest detection with a precision strategy, a DSS was developed by Sciarretta et al., 2019 to manage medfly and Wiedermann in different variety of orchards. It consisted of 3 sub-systems: first, dealing with when to deploy traps; second, talking about the area and type of treatment; and third, focusing on spraying. The results revealed that employing the DSS greatly reduced the number of pesticide treatments, the treated area, and the volumes of pesticide use. Another DSS system was developed by Bourgeois et al., 2015 to facilitate precision farming methods by including field sensors that gather data on humidity and temperature. Wireless networks are used for data transmission, and then with the help of mobile applications, farmers can make decisions about irrigation and other farming tasks. GZ-AgriGIS is another example of a DSS developed for local Chinese farmers that helps them to make decisions, including irrigation and fertilizer application. This system works by sharing site-specific data and by providing scientific advice for crop management by exchanging analytical models (Sha & Zhang, 2007).

The benefits of the integration of ML and DSS are also shown by Suneetha, 2023 with an increase in efficiency, sustainably, and more precision obtained in farming practices. Cloud computing, along with the subsets of AI such as ML, DL, is widely being used. ML is a subset of AI that allows systems to learn from experience and improve automatically. While DL is a subset of ML, it uses complex algorithms and deep neural networks for model training (Joshi et al., 2024).

The AI algorithms, data analytics, and ML approaches provide information about several aspects, including the development of crop, post-harvest management, and crop quality, which is helpful for future research (Arjun, 2013). A combination of AI with the field of agriculture holds great potential for addressing the issues and recent emerging concerns, such as meeting the growing demand, minimising food waste, improving the use of available resources, and dealing with the climatic changes affecting farming practices (Ben-Ayed et al., 2013). Recently, rapid incorporation of AI-based technologies has been seen in farming techniques. The cognitive computing concept involves a computer model of the human thought process. This results in the advancement of farming practices and agricultural technology that are AI-driven. It is important to adapt the role of AI in this field to cope with the emerging concerns affecting the population and the climate. The latest system needs to be more consistent in terms of productivity, efficiency in operation, climate change, and sustainability for the betterment of future generations. To overcome all the challenges, AI is a promising approach (Liu, 2020).

2. AI-based smart irrigation

Smart irrigation applies to the automated, data-driven management of irrigation practices that offers precision, efficiency, and timing as compared to the traditional irrigation system. One of the factors that determines the food security of a nation is the yield of a product. It acts as an accurate indicator of total agricultural productivity and overall returns. Recent advancements, including crop genetics, fertilisation, and AI-based field management, have increased the yields of crops (Luo et al., 2023). Some major benefits of AI-powered systems are the prediction of yields with greater accuracy, and the AI-driven irrigation system leads to better as well as wise use of resources, including water and electricity (Sinwar et al., 2020). The study conducted by Balafoutis et al. (2017) shows the use of Variable Rate Irrigation (VRI), an advanced irrigation system that delivers a precise amount of water using sensors, GPS, and data analytics. It can be done with a self-propelled system or by micro-irrigation. Common systems include Mid Elevation Spray Application (MESA) with 85 % efficiency. Similarly, an irrigation efficiency of 87.97 % was observed as compared to the old traditional method during the cultivation of Geranium psilostemon in a study by Ercan Oğuztürk et al., 2025. This study highlights the use of a smart irrigation system to optimize water usage effectively. Preite & Vignali, 2024 also mentioned that by the use of a multi-layer perceptron neural network, 27 % water saving and 57 % energy saving were achieved. Nowadays, many institutions are working on developing new and modern technologies, for example, IoT, which is currently used for efficient farm management. These technologies eliminate related inefficiencies while aiding in achieving the best outcomes and advancements in their sectors. Farmers can afford the newest technology, but they have limited access to knowledge about IoT (Khan, Kumar, Dhingra, & Bhati, 2021). Despite this, IoT is seen as an emerging technology for sustainable agriculture and is better adapted by farmers to gain improved agricultural production. Smart irrigation is an evolving area of science that increases agricultural productivity while reducing its environmental impact by using data-driven strategies. A deeper understanding of the operational surroundings and the operational activities is made possible by the data that present farming processes gather through various sensors. Thus leading to more precise decision-making (Ben Ayed & Hanana, 2021). In smart irrigation, the irrigation is adjusted as per the changing soil and climate, thus resulting in saving water and simplifying the irrigation process (Abedin et al., 2017). Farmers can precisely understand the field parameters such as temperature, water requirements, weather, and other factors by the use of AI technologies such as IoT, mobile applications, and sensors. This helps in the automation of agricultural practices, thereby efficiently increasing productivity. This allows the farmers to generate harvests while utilizing fewer resources, such as fertilizers, water, and seeds, by employing smart agriculture (Musa & Basir, 2021). Fig. 1 shows an AI-integrated smart irrigation system and shows the technological framework.

Fig. 1.

AI-integrated smart irrigation system.

3. AI-driven approaches for yield prediction and remote sensing

Yield prediction is a key component in farming focused on sustainable production. Timely predictions favor farmers' decisions regarding planting of crops, irrigation, harvesting, and trading. ML has emerged as a key technology for the development of prediction models. ML works by learning a prediction model with the help of past data available, then evaluating the model to test how it responds to the new data, and finally using it to make new, authentic predictions (Leukel et al., 2023). Yield prediction is based on a multi-scale assessment, including field level, i.e., local data sources, and regional or satellite level, i.e., global data sources. Field-scale prediction focuses on parameters such as soil sensors, weather, irrigation schedule, application of fertilizers, harvesting, pest and disease control. At the same time, regional prediction focuses on climate models and remote sensing (RS) data (Jeong et al., 2016; You et al., 2017). Major problems encountered in the prediction process are heterogeneity of data, missing data values, errors in data collection, and climatic variability, which causes fluctuations in yield, making it difficult for models to forecast. Other issues include data quality, incorporation of modern AI tools, interpretability of models, and high cost (Singh, Kandhoua, & Thakur, 2024). Several DL based models that are used for yield prediction are shown below in Table 1.

Table 1.

Overview of ai-based deep learning models for crop yield prediction.

Crop	Data source	Model	Findings	Reference
Soyabean	Satellite data	CNN-LSTM	High prediction performance, satisfactory results for early prediction (RMSE = 329 and R2 = 0.78)	Sun et al., 2019
Corn	Hyperspectral imagery	CNN	75.50 % classification accuracy	Yang, Nigon, et al., 2021
Soyabean	Satellite data	Combined model (CNN + LSTM) named as DeepYield	Accurate prediction of yield for the past 2 years (RMSE = 4.79)	Gavahi et al., 2021
Wheat, corn	Spatial-spectral-temporal data	SSTNN (Spatial-Spectral-Temporal Neural Network)	Higher yield prediction accuracy (Wheat: RMSE = 0.67 and R2 = 0.83; Corn: RMSE = 0.84 and R2 = 0.68)	Qiao et al., 2021
Cereal crop (Wheat, oats, barley)	High-resolution Unmanned Aerial Vehicle (UAV) image data	UAV-based remote sensing, 3-D-CNN	More accurate prediction at the intra-field scale (RMSE = 289.5 and R2 = 0.962)	Nevavuori et al., 2020

Crop yield predictions using RS methods continuously cover broad areas. Applications for RS in agriculture involve damage identification, yield forecasting, and harvest time prediction. Since agricultural productivity depends on a variety of different factors, yield prediction needs the utilization of numerous datasets (Xu et al., 2019). It is necessary to train the models with datasets that show the results based on previous understanding. Statistical models and AI algorithms are used to anticipate agricultural yields since they allow yield estimation through the growing season, as shown above in Table 1. Making choices on the seasonal scheduling of cultivation or storage spaces can be made easier by knowing the anticipated yield in a particular year. It is possible to increase a farm's profitability and equalize the amount of farming equipment employed, for example, fertilizers or other products used for the protection of plants (Hara et al., 2021). It was found by Du et al. (2018) that AI significantly boosts crop productivity by approximately 20 % to 150 %, which is crucial for conserving water resources and sustaining better crop yield.

RS enables the producers to gather, visualise, and perform realistic, cost-effective assessments of both soil and crop health conditions throughout different production phases. It can serve as a warning system to spot potential problems and offer opportunities to efficiently solve them. To gather data at spatial, spectral, and temporal resolutions, several RS platforms are currently in use, including aircraft and satellites (Varela et al., 2018). Without physically visiting a field, crops, or objects, RS provides information about them. The data is collected using a variety of sensors and is accessible in its unprocessed, processed, or analytical forms. It operates with the idea of object characteristics such as chemical, physical, or structural qualities. Sensors acquire the information and transmit it to the system storage, and then the information in the storage systems is structurally arranged. To decide what tasks are necessary to move upwards, this information is analysed (Li & Finch, 2022). Utilizing innovations, predictive analysis gathers the data and knowledge required to identify how production might be enhanced and to implement all necessary corrective actions to achieve the goal. The technologies make it viable to intervene effectively at the right time and right place, responding with exceptional accuracy to the unique requirements of different crops and areas of the farm (Ennouri et al., 2021). RS has become more easily accessible and affordable. Technological improvements in data storage, computing power, the use of DL algorithms for data processing, and tools like satellite, UAS, robot platforms, and IoT have simplified the process (Higgins et al., 2017). Barriers in the adoption of RS technology from test to production environments include inadequate understanding about the efficiency of RS technologies and techno-economic advantage; the shortage of RS decision-support systems and the lack of training in their use; and the lack of compatibility with data and tools from various sources (Khanal et al., 2020).

The validation of predictive models are highlighted in various studies for instance Prity et al. (2024) evaluated ML models for crop recommendation based on three metrics which included Mean Squared Error (MSE), Mean Absolute Error (MAE) and R-squared (R (Abedin et al., 2017)) which indicates prediction accuracy by measuring the difference between predicted and actual values, predicts error and talks about the models explanatory power by showing how much of the variance in the dependent variable can be predicted from the independent factors. Similarly, for prediction accuracy and for interpreting the performance of ML models for yield estimation, Root Mean Square Error (RMSE) and MAE metrics were used by Khodjaev et al. (2025) lower the RMSE and MAE value better the fit between values predicted by the model and yields obtained. These models, along with relative RMSE (rRMSE) percentage, were also used by Sabo et al. (2023) for analysing DL models for yield prediction of crops, facilitating the comparison of yields across different databases. Mean Biased Error (MBE) and Modelling Efficiency (EF) which evaluates the model's predictive power about observable data and averages prediction bias were used in addition to RMSE and R² by in a study done by Gill et al. (2024) for evaluating the statistical and ML models developed for predicting wheat yield among different district in Punjab, India. After obtaining the results from the above-mentioned metrics, the artificial neural network (ANN) proved to be the most accurate approach.

AI models outperform the traditional regression method in their ability to identify complex, non-linear interactions among variables, automatic identification of relevant features, thus resulting in higher accuracy of models and providing more reliable data because of robustness to noise. Another reason is better scalability for handling large datasets and adaptability to different domains (Levent et al., 2025). Research done to compare traditional and advanced models on economic prediction by Narendran, 2024 shows that advanced DL models (LSTM and Gated Recurrent Unit) offer superior accuracy, prediction, and low error as compared to traditional series models

4. Crop improvement and AI

Food security for almost 1.3 billion people is provided by the Indian agriculture sector, which contributes 18 % of India's GDP. Agriculture makes up 10 % of all exports from India and is the fourth-largest commodity sector. India still relies on agriculture, which uses a lot of resources (Kumar et al., 2020). Crop production needs to double by the year 2025, translating to a 1.75 % rise in total agriculture production yearly, to fulfill the demand of an increasing population (Steensland & Zeigler, 2021). The major challenges for sustainable agriculture include a changing climate, scarcity of water, lack of labour, and land. Better crops and better agricultural management are the two main approaches that can be used to solve these problems. Crop improvement strives for the formulation of new varieties of crops that are more adaptable to changing environmental factors, such as salt soils, and have larger yields and higher quality. On the other hand, better agricultural management strives to improve agricultural ideas similar to precision farming, which maximises output while minimising inputs in agricultural farming systems (Houle et al., 2010). A crucial step in anticipating effective solutions is helping investors and farmers implement sustainability in agricultural practices, notably with the use of technologies like cloud computing, AI and IoT (Ben Ayed & Hanana, 2021). To handle agricultural concerns, AI tools propose algorithms that can assess performance, detect unforeseen problems or occurrences, such as consumption of water and irrigation management by setting up smart irrigation systems (Suprem et al., 2013). AI is being applied in areas like weather forecasting, agricultural analysis, and by automating the process of precisely detecting disease or pests. Additionally, AI streamlines the planting, harvesting, and commercialization of crops. So, through improving crop management techniques, AI developments have helped agro-based businesses operate more effectively, allowing a range of digital firms to make investments in algorithms that help agriculture and address farmer concerns (Ben Ayed & Hanana, 2021). One such example is an AI-based sowing application developed by Microsoft and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). The pilot study conducted on 175 farmers growing groundnut in Andhra Pradesh using this application shows that the farmers who postponed the planting of seeds for three weeks as per the guidance of the sowing application had 30 % more yield per acre (p < 0.05) as compared to others (Kumar et al., 2020). Also, farmers can manage weeds effectively and precisely by using AI approaches, including robotics, ML, and computer vision, to spray pesticides only in areas where there is the presence of weeds. As a result, less chemical spray will be needed to cover the whole area (Kamilaris et al., 2018). During the first phase, ML technologies are utilised to forecast soil characteristics, crop output, and irrigation needs. During the production process, ML might also be utilised to detect diseases. ML-based algorithms are utilised to predict production planning during the processing phase to ensure high product quality. Furthermore, the distribution cluster may benefit from ML algorithms in terms of storage, consumer analysis, and transportation (Hatem et al., 2022). The intelligent crop growth surveillance system is mostly used in greenhouses, where the target crops plus their growing environment are monitored using a Time-of-Flight (ToF) sensor, soil sensors, a micro weather station, and red green blue (RGB) camera. Additionally, this work will support agricultural specialists in creating biological pest and disease defences for crops (Huang et al., 2022). Several studies show the utilization of AI for crop improvement, such as AI-based crop diagnosis and advisory tool known as “Plantix”, has been proven to be beneficial for the farmers. Akinyemi et al. (2023) tested the accuracy of this mobile application on staple crops grown in South-Western Nigeria for detecting pests and diseases. About 90–100 % accuracy was achieved when tested on maize, okra, cassava and plantain. AI-based satellite technology helps in predictive analysis. aWhere and Farm Shots are two companies that use ML and satellite data for weather prediction, analysing images obtained from drone cameras to detect pests, disease and deficiencies in crops (Giri et al., 2020). A study by Jain et al. (2019) shows that the effectiveness of interventions is doubled by the use of satellite images, as it leads to better yield and efficiency, concluding that it is a sustainable approach to increase food production. Ozor et al. (2025) shows the collaboration between small-scale farmers from Nigeria and AI-driven approach. Artificial Intelligence for African Food Systems (AI4AFS) introduced on-farm disease detection, IoT irrigation systems and e-extension applications to the farmers. The results depict 40 % improvement in terms of water efficiency, and an increase in cultivated land from 1 to 2 ha to 4–5 ha. The e-Choupal initiative by ITC promoted the adoption of digital technologies by Indian farmers, including smart irrigation, weather forecasting, and satellite use. As a result of this initiative, the farmers from Andhra Pradesh growing chilli crop have seen a 13 % and 27 % rise in productivity and net returns, respectively (Rao, 2023). A comparison between AI-driven approaches and traditional agricultural techniques, highlighting their relative benefits and constraints, is shown in Table 2.

Table 2.

Comparison of ai-driven and traditional agricultural techniques.

Traditional approaches used	AI-driven approaches	Relative benefits	Constraints	Reference
Manual labour-based decision making	Precision farming, autonomous machinery, predictive analysis, and drone technology	Improved efficiency, improved yield prediction, and reduced cost of labour workers, crop monitoring, and disease prevention	Higher cost, requirement of technical expertise, and infrastructure	Akintuyi, 2024
Manual crop monitoring and the traditional method of irrigation	All-terrain vehicles (ATVs) are based on automation of tasks, including planting, weeding, harvesting, etc.	15 % to 20 % increase in yield, 25 %–30 % reduction in overall investment	Requires training, low accessibility in remote areas.	Padhiary et al., 2024
Old water-based irrigation system and weeding practices	Robotic system for weeding and irrigation	Saving water, preserving soil fertility, limiting use of herbicides, pesticides, and effective use of labour	High initial cost and the requirement of infrastructure	Talaviya et al., 2020
Manual mapping and survey methods	Use of ML, DL, and ANNs for agricultural mapping	Enhanced land use mapping, crop identification, and disease control	Requirement of technical expertise	Espinel et al., 2024

5. Economic evaluation and investment models for ai in agriculture

Variable Rate Nitrogen Fertilizer Application (VRNFA) is one of the precision agriculture technologies that highlights the farm productivity and economic benefits. Excessive use of nitrogen fertilisation adds to additional cost, reduces financial returns, and has negative impacts on the environment. On the other hand, an insufficient application results in reduced yields and income. The yield gain resulting from the use of VRNFA eliminates the requirement of extra incentives and results in generating additional revenue by reducing the need for extra labour and fuel (Biggar et al., 2013). The economic assessment based on wheat agriculture by Tekin (2010) shows a 1 to 10 % increase the production and a 4 to 37 % saving of nitrogen fertilisation by the use of VRNFA. With a 5-year depreciation period and a 21 % rate of interest rate, the annual cost of adopting precision farming equipment for farms between 50 and 500 ha varies from $13 to $131 per hectare. Another study by Balafoutis et al. (2017) highlighting the positive impact of precision farming on productivity and economics shows that an annual cost saving of €5000 to €10,000 is achieved by reducing the use of water and fertilizer by 10–15 % by the use of AI based technologies such as sensors, drones and other software. On average, the annual income for small farm holders lies between €1000 to €3000; hence the initial investment cost for the adoption of AI-based technologies represents a significant amount. However, factoring in the reported annual savings and yield increases up to 30 %, the payback period typically ranges from 2 to 4 years. On the other hand, large agribusinesses, having significantly higher revenue margins and operational scales, often experience a shorter payback period of less than 1 to 2 years, as they can integrate AI across multiple operations such as precision spraying, weather forecasting, and disease detection to maximize returns more quickly.

Similarly, a study by Liakos et al. (2018) assessing the application of AI on smart farming found that crop monitoring systems based on AI enhanced production by 10–15 % while lowering input costs by 12–18 %. Oliveira and Silva (2023) studied the benefits, trends, and challenges of AI in agriculture and emphasised that although the initial costing of hardware, software, drones, automated machinery, IoT sensors, and model development may be high but is outweighed by the long-term advantages, such as higher yields and resource efficiency.

5.1. Existing funding models

Certain government grants and initiative funds the AI modelling in the field of agriculture, such as the Agriculture and Food Research Initiative (AFRI) by the USDA National Institute of Food and Agriculture (NIFA), supporting AI-based research for agricultural systems. Presently, autonomous robots and smart sensors are being developed under this (Van Goor et al., 2019). A national AI-funded institute that focuses on fundamental AI advancements in the field of agriculture was established in 2020 by the name Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability (AIFARMS). As a result, several collaborative initiatives with close partnerships focused on AI approaches for agriculture have been developed, working for the advancement of this field (Adve et al., 2024). AI Institute for Resilient Agriculture (AIIRA) is another initiative that focuses on enhancing sustainability and agricultural resilience, The main objective is to create AI-powered solutions that enhance crop resilience, promote environmentally friendly agricultural practices, and optimize resource utilization. NIFA and the National Science Foundation (NSF) have contributed a $20 million grant for this initiative (Ganapathysubramanian et al., 2024). Two international funding programs, i.e., Consultative Group on International Agricultural Research (CGIAR) Challenge Program on Water and Food and United States Agency for International Development (USAID) Global Hunger and Food Security Initiative, also provide funding for AI modelling research (Sarku et al., 2023). Although the global adoption of AI is increasing because of its transformative approach, but still the growth remains uneven in low-income countries (LICs). They may gain significantly from AI-driven development, but numerous barriers hinder the implementation and deepen the existing worldwide inequalities in access and utilization of new technologies, such as limited partnership with technology providers and poor infrastructure facilities. The accessibility of these initiatives is predominantly concentrated in developed nations, the reason being better infrastructure, availability of technical experts, and well-established regulatory systems. To overcome this gap, the developing countries and LICs need to overcome the challenges related to trained human capital, digital infrastructure, institutional effectiveness, and effective implementation of policies. AI‑leading countries and key organizations such as the World Bank, United Nations Educational, Scientific and Cultural Organization (UNESCO) and USAID can significantly support LICs through technology transfer, targeted funding, and capacity-building initiatives (Khan et al., 2024).

6. AI in post-harvest management and cold chain logistics

The stage that begins immediately after harvesting in the crop production cycle is known as post-harvesting and includes handling, processing, storage, packaging, transportation, and distribution. After harvesting, a significant amount of leftover waste raises concerns. Post-harvest losses are brought on by internal and external factors, including physiological deterioration, mineral deficiencies, low or high temperature injury, or unfavourable environmental conditions. Post-harvest losses are a major contributor to waste, which range between 2 and 20 % in industrialized countries and between 24 and 40 % in underdeveloped countries (Ferrández-Pastor et al., 2016). AI frameworks in the food industry help in the preservation of seasonally available perishable food commodities. To track the physical state of materials during transportation, Radio Frequency Identification (RFID) and IoT sensors have been developed. These models are also capable of detecting diseases, rotten fruits, and low-quality food components (Zhang, 2021). For instance, Kollia et al. (2021) explored the use of DL architectures such as CNN and LSTM networks for predicting yield and reducing energy consumption for refrigeration systems. These AI approaches have resulted in enhanced food quality. The LSTM-AM model outperforms other conventional ML techniques, such as SVR and RFR, in terms of prediction accuracy, with MSE error being significantly lower at 0.002 as compared to 0.015 and 0.040 for SVR and RFR, respectively. The Fully Convolutional Networks (FCN) model showed an accuracy of 98.20 % for expiry date detection. A tracking system that talks about the shelf-life expiry date was developed by Mamidala, 2023 using ML algorithms. It works by predicting spoilage based on colour, texture, and smell of food. This has resulted in reducing food waste by monitoring the freshness of perishable food. In a study by Li et al. (2021) a model was proposed for grading and evaluating the quality of apples based on CNNs. The results show 99 % and 98.9 % training and validation accuracy, respectively. Similarly, another CNN-based model was developed by Wang and Zhang (2019) to classify harvested apples as per their size, and the presence of any surface defect was also detected. High accuracy and improved market value were observed with minimum use of manual labor. The use of Support Vector Machine (SVM) was shown by Patel et al. (2021) to identify and segregate defective mangoes after harvesting during packing, thereby improving the post-harvesting quality.

One critical component of post-harvest management is effective cold chain logistics (CCL), which influences the shelf life of fresh produce and perishable food items. The visualization of cold chain data flow is shown in Fig. 2. Improper cold storage leads to significant post-harvest losses, which may exceed by even 30 % (Kitinoja et al., 2011). CCL is the food supply logistics system that uses refrigeration technology to consistently maintain ideal conditions for temperature and humidity environment for perishable items like fruits, vegetables, dairy, meats, and fish (Ndraha et al., 2018). A substantial amount of electricity is required for fresh foods, and it is a significant financial burden for all parties involved in the cold chain, including producers, suppliers, and retailers, throughout the cold chain. This atmosphere must be adequate and stable in terms of temperature and humidity (Han, Zhao, et al., 2018). Even after harvest, fruits and vegetables remain biologically active and show vital signs. The continued consumption of organic materials like sugars and starches by living biological organisms during respiration, evaporation, and ethylene release results in a decline in the quality of fresh produce. This results in lowering nutrient value, functional properties, hardness, and poor shelf life (Han, Li, et al., 2018). Fresh produce undergoes chemical, physical, and biological alterations as a result of external variables like compression, movements, changing temperature, levels of ethylene, O₂, and CO₂, as well as internal variables like heat and moisture diffusion within the produce across the entire supply chain (Mercier et al., 2019). According to the Food and Agriculture Organization (FAO), food losses in developing countries tend to happen during the post-harvest phase, caused by improper handling, transportation, and storage. The main reason behind this is a lack of suitable infrastructure and knowledge about handling perishable items (Goedhals-Gerber & Khumalo, 2020). FAO statistics show a 30–40 % loss of the total produce before it reaches the market. The percentage distribution varies with fruit, vegetables, and root crops accounting for 40–50 %, fish and cereals 30 %, and oilseeds 20 % (Food and Agriculture Organization of the United Nations (FAO), 2025). The market currently offers a wide range of promising technologies like IoT, AI, cloud computing and big data to enhance CCL's service level, one of the key areas for research and innovation in future for upgradation and transformation of CCL into systems that are digital, intelligent, effective, and ecological (Han, Zhao, et al., 2018).

Fig. 2.

Flowchart of cold chain logistics operations.

6.1. Real-time monitoring and early warning systems in cold chain logistics

For fresh agricultural produce, effective CCLs depend on real-time data flow to monitor temperature, humidity, and conditions at the time of transportation. Now, continuous monitoring and tracking of perishable food items is possible by IoT-based CCL systems through the integration of smart technologies such as wireless sensor networks (WSNs) and RFID tags, and GPS trackers. This helps to maintain optimum storage conditions and increases the life of fresh produce during longer transit times (Yu et al., 2021). For example, a WsN-based monitoring system was developed by Qi Lin et al. (2012) for aquatic CCLs, showing more accuracy for the collection and transfer of environmental data that is required to maintain the quality of the product during transportation. Similarly, an IoT-based real-time monitoring and energy consumption management approach for CCL was given by Wang and Du (2025). This study integrates an LSTM network along with the Particle Swarm Optimization (PSO) algorithm for developing an energy consumption management strategy by utilizing a distributed IoT system and various data transmission protocols. Experiments conducted in a simulated cold chain scenario, using multi-sensor data, demonstrate that the proposed system outperforms traditional models in efficiency, achieving improved control accuracy and reduced energy consumption.As per this study, the traditional CCL model achieves 85 % accuracy for temperature and humidity control with a variation range of 2.5 °C. A slightly lower accuracy of 82 % with a wider range of variation of 2.8 °C was shown by the CCL system utilizing fixed power regulation. On the other hand, a superior performance with 91 % control accuracy and a reduced variation range of 1.8 °C was shown by the LSTM-based prediction optimization model, also, the optimization effectiveness is improved by 7.1 % over the traditional approach.

Due to several internal and external factors, there is a high risk of food deterioration at any point in the cold chain. One of the main reasons for this is fluctuations in temperature; a CCL system appropriately monitors food safety control of perishable food parameters. Notifying warning signs and predictive analysis based on AI helps identify changes in storage conditions and send out automated alerts through mobile apps and wireless networks (Kale, 2022). Recent advances in CCLs highlight the incorporation of blockchain technology to safeguard data exchange between stakeholders. Tian (2016) mentions that logging each transaction change in a tamper-proof ledger, blockchain-based cold chain tracking systems increase accountability and transparency; hence, any weak links are identified in the supply chain.

7. Applications of AI in food supply chain and post-harvest management

7.1. Fast-moving consumer goods and AI

The agricultural industry has implemented several AI-based farming techniques. The concept of cognitive computing involves simulating human thought processes on computers. This leads to an AI-powered agriculture system that provides its assistance in interpreting, learning about, and responding to various situations to increase efficiency (Xia et al., 2019). The agriculture and farming sector has been the mainstay of the food industry since the beginning. The fast-moving consumer goods (FMCG) industry relies on raw materials for the processing and manufacture of products; hence, increased food production enhances the availability of raw materials for this industry [44]. The COVID-19 epidemic has adversely affected the supply of these sectors and countless lives. As mentioned by FAO, the percentage of hungry and malnourished population has recently increased. However, the agricultural industry can address several issues that affect crop output and improve both the quantity and quality of food ingredients by integrating AI and ML into the management of crops and automated systems (Zhou et al., 2023). Fig. 3 shows how AI has affected the FMCG and food industries. This section discusses a few of the ML innovations that have been used in agriculture and helped to enhance crop management.

Fig. 3.

AI application in FMCG and food industries in Argo.

7.2. Grain grading and sorting

Manual grain inspection is a labour-intensive and error-prone procedure, and it could result in selecting poor-quality grains. There are several factors which may affect the worker's ability to sort grains, such as fatigue, the presence of obstacles and poor lighting conditions. That is why computer vision systems are gaining popularity. These systems can efficiently examine grain images to identify flaws and impurities such as cracked kernels, the presence of any kind of contamination and fungi by the use of modern imaging techniques and ML algorithms (Taneja et al., 2023). To classify and identify grain and other items, the agriculture sector has some commonly applied computer vision techniques such as ANN, Dense Scale Invariant Feature Transform (DSIFT), and SVM. Wheat grains are classified based on size, shape and colour using ANN. Several other features such as texture, shape and type are recognised by DSIFT and wheat grain that has been germinated and milled rice grain are analysed using SVM (Zhou et al., 2023). A robotic system that uses of CNN and DL model to analyse images for automatic grain grading of root-trimmed garlic is one example of AI being used for this purpose (Huyet et al., 2020).

The assessment of different stages, such as maturity, decaying and damage, is included in the concept of farm sorting. The parameters of the product can be assessed at the basic input level only with the help of AI-based systems. This includes the use of laser technology, X-rays, a camera with high resolution and infrared (IR) spectroscopy. The use of all these technologies helps in determining different contaminants present and product defects, thus facilitating decision making and improving product quality (Taneja et al., 2023). AI models can solve tasks related to sorting, maturity assessment, identification, quality and damage detection of fruits, such as apples, coconuts, blueberries, etc. Machine vision may be a viable option to improve harvest efficiency, as it offers an alternative to automated and non-destructive sorting in the field. Computer-controlled hydraulics have been highly beneficial in achieving significant savings and meeting business requirements (Abbas et al., 2019). AI models have been integrated into on-farm sorting, as well as significant advancements in ML, DL and machine vision (MV). Anns (Annuities-Based Models) can provide reliable pattern identification and are used in the hyperspectral image analyses for chemical identification, such as Mineral Nutrient, Dry Matter, Visible Image Size, External Defects, and more. Visual imaging has been employed to assess the fruit's weight, size, colour, flaws, and bruising through the use of CNN models, which are designed to replace manual inspection of the fruit (Masakowski, 2020).

7.3. Support vector machines in sorting and associated bottlenecks

SVM is one of the ML approaches that could be used to provide answers for improved crop management. It is used for binary and multi-class classification (Kok et al., 2021). It is widely used in sorting applications because of its accuracy and robustness with smaller datasets. In a study conducted by Patel et al. (2021) the application of SMV is demonstrated by showing the use of SMV for sorting mangoes according to ripeness, utilizing visual attributes like colour and texture to achieve high accuracy. Also, the findings from a study by Bharadwaj et al. (2012) show that using SVM with discretized data provides better accuracy and is a viable method for enhancing agricultural decision-making since it helps to manage complicated agricultural data. SVM was also used by Pooja et al. (2017) to classify plant leaf images to detect defects such as mildew, rust, and blight. Using colour and texture, the study demonstrated a high degree of accuracy in distinguishing between infected and healthy leaves. In another study by Barman and Choudhury (2020) classification of soil texture was done using multi-class SVM to classify soil types based on pH, nitrogen, potassium, and moisture content by and an accuracy of 91.37 % was observed for all soil types. A model based on Genetic Algorithm (GA) and SVM was developed by Xu et al. (2021) to automatically grade apples. The apples were accurately classified into different grades based on their colour, texture, ripeness, and shape extracted from images obtained. The most relevant feature was selected by GA, thus improving the SVM performance. A classification accuracy of 92.3 % was observed for the same. These studies show that SVM is a powerful tool in agriculture, however, some bottleneck issues must be addressed, such as SVM training being expensive when dealing with large datasets. This problem is frequently encountered in precision agriculture when data is gathered from sensors, drones, and satellite imaging. Graphics Processing Units (GPU) -based acceleration was suggested by Paoletti et al. (2020) to overcome the training time and memory usage as bottleneck issues. Another issue is imbalanced data sheets and slow prediction speed that cause poor classification and have a negative impact on SVM performance (Anguita et al., 2010).

7.4. AI-assisted food packaging solutions and transportation

A developing technology called intelligent packaging is used to help make choices easier while enhancing the quality and safety of food. The innovations in smart packaging technologies include barcode labels, tags with RFID, bio sensors, time, temperature and gas indicators (Yam et al., 2005). AI can be used to develop products by finding the most cost-effective and sustainable packaging options for food and drinks. However, packaging has some limitations, such as its potential impact on food; for instance, some printing inks are not appropriate for packaging high-fat foods, like cheese, as they may cause chemical contamination (Taneja et al., 2023). Neural networks, fuzzy logic, as well as genetic algorithms can be used to modify the packaging process so that it finds the most appropriate materials according to these guidelines, resulting in lower costs. Also, visual sensors can be used to find the best way to package food, as they reduce the amount of plastic surface area required to cover things with abnormal shapes (Masakowski, 2020). A battery-free AI-smart packaging system was developed by Douaki et al. (2025). By the use of this system, the shelf life of fish was increased up to 14 days, It works by continuously monitoring the freshness and releasing active chemicals to prevent spoiling. Another example is the robotic food packaging discussed by Drijver et al. (2023) which shows 99.8 % accuracy rates for packaging and 100 % accuracy rates for filling containers, showing much better results over traditional ways.

On-farm sorting entails removing contaminated, rotten, and damaged food before classifying it according to size, colour, maturity, and ripening stage in bins or trays (Majeed & Waseem, 2022). From picking location to collection location, which is often at the end of rows, the bins and trays are transported. Transporting trays and bins to the collection centre is known as on-farm transportation. The factory's sorting line is where huge fruit-producing enterprises carry out the processes of sorting and moving produce for packing (Zhou et al., 2023). However, these duties might be carried out in the field or a storehouse on small farms without an integrated production line. For small-scale commercial farming and orchards, on-site sorting and transportation are crucial since the product is first brought to storage in bins before being offered on the new marketplace (Bader & Rahimifard, 2020). According to Zhou et al. (2023) the application of computer vision and ML to on-farm sorting and transportation reduces the losses that occur during post-harvest by increasing the accuracy and speed of the entire process. Fashina et al. (2024) also mentioned a reduction in overall costs and a decrease in delivery time by the use of AI-driven models because of their ability to predict demand, manage inventory, and streamline transportation routes. Sorting and transportation done on the farm are demonstrated in Fig. 4. These techniques have been employed as an alternative, practical technique to substitute manual sorting and transportation since the emergence of AI technology (Idama & Uguru, 2021).

Fig. 4.

AI and transportation.

The development of ANN makes it easier to track goods and eliminates inventory forecasting issues. Harvesting aids like co-robots for transportation have been created to help the harvesting of crops like grapes, apples and strawberries to address the concerns of high cost labour, poor harvesting and accidents at workplace (Zhou et al., 2023).

8. Machine learning in plant breeding

Plant breeding is well acknowledged as a successful plan to end hunger in the world and represents one of the most effective methods for enhancing security of food security in future. Plant breeding has been employed in recent years to develop new crop cultivars which are more productive and have higher resistance to environmental changes, infections, and pests (Yoosefzadeh-Najafabadi et al., 2022). Breeders can understand the genetic basis of plant traits, develop more effective breeding plans, and draw meaningful conclusions from their data by analysing varied information using techniques from AI, ML, bioinformatics, and high-speed computing (Zhang, 2021). Fig. 5 depicts the application of ML in plant breeding. Although there are a lot of potential advantages to utilizing ML in plant breeding, some obstacles must be overcome before its use in breeding programmes. Large, high-quality datasets and hence substantial computational resources are needed for ML algorithms (Yoosefzadeh Najafabadi et al., 2023).

Fig. 5.

Applications of ML on plant breeding.

Traditional breeding methods have been transformed by the integration of AI through the use of computational tools to analyse extensive data. AI's advantages in breeding include accurate selection, prediction of complex traits with more accuracy, faster breeding cycles, better data management, enhanced disease detection, and reduction of cost (Farooq et al., 2024). Nonlinear ML algorithms can help to make better yield projections and analyse nonlinear relationships between yield components. For trait prediction, genomic selection, and auxiliary decision making, several DL models and algorithms, such as SVM, Random Forest (RF), ANN, Gradient Boosting, and CNNs, are widely used. A successful use of AI algorithms can be seen in trait prediction, genotype-environment interaction modelling, and genomic selection. Montesinos-López et al. (2018) mentioned that several ML algorithms, such as SVM, RF, and Gradient Boosting, are employed in breeding populations to predict complex traits at the genetic level. Similarly, Singh et al. (2016) mentioned the use of several ML-based algorithms, such as SVM for thermal light and fluorescence imaging spectroscopy, SAM for remote sensing, and ANN for RGB images for determining diseases in plant species. Additionally, AI is incorporated into DSS to optimize and predict breeding strategies and hybrid performance. Genomic Selection Decision Support Tool (GS-DST) helps estimate breeding values (Crossa et al., 2017). Gradient boosting machines (GBM) were used to predict the level of aflatoxin in lowa corn by Branstad-Spates et al. (2023) and an accuracy of 90.32 % was observed. The GBM method was also used for corn yield prediction, and a higher accuracy rate for optimal seeding prediction was observed than with traditional methods. A 6.2 % increase in corn yield was observed as compared to uniform seeding (Du et al., 2022). Highlights the ability of AI to enhance the climate-resilient trait by the use of RF, SVM, and neural networks. The multi-omics insights that include genomics, transcriptomics, proteomics, and phenomics help in the selection of superior breeding lines that are resistant to climate extremes (Khan et al., 2022).

8.1. Several applications of ML and plant breeding

In their lifecycle, plants are exposed to a variety of biotic as well as abiotic stresses. Various approaches are employed to determine superior genotypes and assess stress tolerance and resistance. Traditional ML methods combined with deep CNN are used to identify crop stress and various plant diseases with 95 % accuracy. ML techniques combined with imaging techniques can simulate and predict the genotype's responses under stressful conditions. It can also be used to predict which variant will be more resistant to stress and under non-stress conditions (Van Dijk et al., 2021). The integration of Genomic and phenomic data with ML is shown in Fig. 6. ANN is the best ML tool for this, and has shown great results when used for studying the effects of atmospheric pressure, precipitation, temperature, crop disease, and Multilayer Perceptron - ANN (MLP-ANN) (Niedbała et al., 2020). It is crucial to carry out a careful investigation of the gene action related to phenology, morphology, and yield component characteristics to determine the heritability of a crossbreeding programme. For the selection of superior hybrid varieties, better parental combination prediction is crucial. ML-assisted ANN algorithms can be used to comprehend the parental blends to achieve the best breeds (Gakhar, 2021).

Fig. 6.

Integration of genomic and phenomics data with machine learning.

a) Biotic and Abiotic Stress Assessment.

The growth and productivity of most vegetable crops are heavily influenced by plant diseases or abiotic conditions like salinity or a lack of water. Recognition and early detection of biotic and abiotic stresses would enable early action to regulate and prevent the spread of infection, or alter irrigation management procedures before the entire crop is harmed and significant crop losses take place (Savary et al., 2012). Before symptoms appear, imaging sensors can spot the beginning of hazardous effects. Since hyperspectral imaging uses high-fidelity plant reflectance information over a wide range of the light spectrum, beyond of human vision, and captures more than the customary three bands of coloured light found in traditional digital imaging, it is preferred among imaging techniques for the detection and classification of early phases of abiotic stressors and foliar diseases in plants, from lab to the field scale (Navarro et al., 2022). Many biochemical and physiological pathways are involved in the plant's response to environmental stresses, so a combination of phenomics, genomic and metabolomic data can be a good strategy for biotic & abiotic stress assessment. Traditional ML methods combined with deep CNN are used to identify crop stress and various plant diseases with 95 % accuracy. ML techniques combined with imaging techniques can simulate and predict the genotype's responses under stressful conditions. It can also be used to predict which variant will be more resistant to stress and under non-stress conditions (Van Dijk et al., 2021).

b) Classification and assessment of genetic diversity and Indirect Prediction, and Yield Component Analysis.

Genetic diversity is a crucial criterion for plant breeding programmes and is examined through the use of physiological, biochemical, and morphological indicators. Additionally, genetic and molecular markers are commonly used to measure diversity. Common multivariable analytical tools used for these studies include Principal Component Analysis (PCA), Discriminant Function Analysis, K-NOVA, SVM, and Cluster Analysis. Although these tools are time-consuming and often require feature extraction, ANNs can be used to implement object-oriented detection in genetic diversity assessments with high accuracy (Van Dijk et al., 2021). For most plant breeding programmes, the main goal is to get a higher and better output yield. This is usually due to the low inheritability of the crop, which is usually caused by the strong influence of environmental factors. This complexity is one of the biggest challenges that plant breeders face. To improve the yield, researchers tend to select highly correlated traits that have a better effect on the outcome. Common statistical approaches include multiple regression analysis, principal component analysis, correlation coefficient analysis, and path analysis. However, these methods are based on linear relationships of dependent variables over multiple independent variables, which can often lead to inaccurate predictions. Nonlinear ML algorithms can help to make better yield projections and analyse nonlinear relationships between yield components. ANNs have shown great results when employed to study the impacts of atmospheric pressure, precipitation, temperature, crop disease, and Multilayer Perceptron - ANN (MLP-ANN) (Niedbała et al., 2020).

9. AI-driven solutions for soil and pest management in agriculture

Agriculture is based on soil. A crop grows more quickly because of the nutrients in the soil. The moisture content, temperature, and pH of the soil, as well as other chemical and physical characteristics, have a significant impact on crop output. Field-useable open-source hardware is capable of sensing these features (Bhatnagar & Chandra, 2020). Agriculture, agricultural science, and soil analysis are now progressively utilizing digital technologies, such as sensors, IoT, ML, AI, big data, as well as the utilization of drones and GPS satellites (Ramesh & Rajeshkumar, 2021). Using either a DL based tool or image capture with a camera recognition tool, AI and ML technologies have made it possible to track soil characteristics in farms, such as quality, fertility, microorganism, and nutrient deficiency, as well as flora patterns. Perception of images AI can collect and understand this data far more quickly than humans can, allowing it to track crop health, anticipate yields with greater accuracy, and spot crop malnutrition (Chellaswamy et al., 2020).

Plant or crop diseases, as well as insect infestations, are major sources of concern for farmers, since they can significantly reduce yield and profitability. Manually identifying illnesses and pests at an early stage necessitates extensive knowledge and expertise for any individual, and predictions are not always accurate. The advancement of ML in AI has made it very simple and quick to detect various diseases and pests. ANN trained on a vast dataset of images of plant disease and pests generated a prediction model. As a result, the model gains knowledge about plant diseases and pests and becomes extremely accurate at predicting them (Shet & Shekar, 2020). Pests are one of the farmers' biggest threats, causing agricultural loss. AI platforms.

employ satellite photographs and compare them to historical data using AI algorithms to determine whether an insect has landed and what sort of insect has landed, such as a locust or a grasshopper and send notifications to farmers' cell phones so that they may take necessary measures and perform necessary pest management; therefore, AI assists farmers in pest control (Singh et al., 2022). Pesticides can contaminate land, water, vegetation, and other crops if applied excessively. Pesticides can even be dangerous to birds, fish, useful insects such as bees, and nontarget species or plants. The advancement of AI and robotics has opened up new possibilities. These methods allow herbicides and insecticides to be sprayed accurately and only on harmful pests while sparing plants and other beneficial insects. Drones are now employed to spray crops, and they provide automation capabilities that potentially replace some labour-intensive chores (Balaska et al., 2023). The sensors, GPS, and laser on the board, the drone can easily adjust its position and spraying rate in response to the field's height, speed of wind, geology, and terrain. The robot rovers have cameras and image sensors so they can move on their own through agricultural areas. These sensors are capable of taking pictures of the crops. The DL neural network model uses the photos as input to identify pests and dangerous insects in the field. It may then trigger the actuators that control pesticide sprayers to precisely target the bug that needs to be treated (Shet & Shekar, 2020). A comprehensive overview of AI applications in different agricultural domains is shown in Table 3.

Table 3.

Comprehensive overview of ai applications across agricultural domains.

Application of AI in agriculture	AI-based concept	Benefits	References
Yield Prediction	The multilayer perceptron (MLP) model Artificial neural networks (ANNs)	Precise estimation of agricultural yield (RMSE = 0.06173, R2 = 92.84) More sustainable and secure food production	Adhaileh et al., 2022
Smart Irrigation	IoT ML	Water-Saving Irrigation (WSA) is achieved Addressing the issue of water scarcity 98.3 % recognition rate and RMSE = 0.12	Tace et al., 2022
Post-harvest management	ANNs Support Vector Regression (SVR)	Determination of ripeness based on the firmness acoustic impulse. Determination of quality indices based on colour	Kutyauripo et al., 2023
Cold chain logistics	IoT	To track the status of supervised food products in real-time Used to resolve tricky issues like load planning and route planning	Liu, 2020
Transportation	Probabilistic localization algorithms, ANN, CNN	Accelerating highly accurate sorting and transportation on farms, Firmness detection (RMSE = 0.539 R2 = 0.724), ripeness (RMSE = 1.08)	Zhou et al., 2023
Plant breeding	ML Robotics	Elevating breeding information systems. Leverage past breeding experience and knowledge, enriching ongoing programs.	Khan et al., 2022
Soil health monitoring	ML Deep CNN	Possible by use of sensors and ML algorithms. Parameters such as oil nutrient content, moisture content, temperature, and sunlight intensity can analysed by sensors.	Wadoux, 2025

10. Challenges and limitations of ai in agriculture

One of the major challenges in precision farming is security threats that can cause harm to stakeholders. The security issues include unauthorized access to confidential data, financial losses, and many more. It is crucial to ensure the protection of data while integrating AI and cloud-based platforms in agriculture, as privacy concerns affect the farmer's participation in new AI-based technologies and data collection. Amiri-Zarandi et al. (2022) explains the privacy concerns in smart farming and suggests privacy assurance through data lifecycle, i.e., safeguarding the data obtained from farmers at different stages, including information gathering using sensors or drones, storing the data in cloud servers that are password protected, and secure data deletion so that no one can retrieve older information. This will prevent data threats and misuse. To enhance privacy in smart agriculture, a homomorphic signcryption system based on hyper-elliptic curves (HEC) was developed by Taji and Ghanimi (2024) to secure data and maintain the confidentiality of data when it is being transmitted and stored on cloud platforms. Farm systems and networks may become more vulnerable if devices have low security levels, such as those with unencrypted passwords. Hence, farmers should enquire about the security aspects of the devices from suppliers before their installation to lessen this problem. Additionally, farmers can request that encryption to be used for all data, whether it is in motion or at rest (Hazrati et al., 2022). Multi-factor authentication sets up extra security by the use of one-time passwords, face recognition, or fingerprint, and can overcome security issues such as security theft by connecting to an open Wi-fi network (Yang, Shu, et al., 2021).

Although there is now a lack of knowledge about high-tech ML applications in farming all over the world, but AI still provides huge promise for agriculture programmes. Poor knowledge level and lack of digital literacy are another barrier that hinders the adoption of AI technologies. To overcome these barriers, training and extension programs, an AI-powered chat box, and digital literacy via mobile phones must be implemented in the local language. As a channel for information dissemination among farmers, AI-powered chatbots must be designed to cater to the linguistic and contextual needs of a heterogeneous group of farmers. For example, the chatbots can be integrated with multilingual natural language processing (NLP) capabilities for supporting dialects in the preferred local language. This enables the farmer to get the solution to problems (Mittal & Mehar, 2016). In addition to this, voice-enabled interfaces can be integrated to help the farmers with poor reading skills, also for remote areas with poor internet connectivity, SMS service can be used to deliver information about weather, market alerts, or farming tips designed in the local language. Along with this, Interactive Voice Response (IVR) can be used for delivering voice messages (Singh, Wang'ombe, et al., 2024). It was shown Mulungu et al. (2025) that information communication technology (ICT) interventions, such as messaging, educational videos, awareness regarding voice calls, and training guidelines, significantly improved the awareness of farmers and inculcated good agricultural practices. It was also mentioned by Meera et al. (2004) that the ICT-based government initiatives, such as the Gyandoot project, Warana Wired Village project, and iKisan Project, significantly improved the knowledge level of farmers with the help of training provided.

The agriculture field is exposed to outdoor elements such as different weather conditions, soil, and insect attack susceptibility. One of the challenges in developing algorithms and producing accurate predictions is data quality. Large data quality can be a severe issue (Joshi et al., 2024). Another obstacle is the high cost of the different cognitive farming choices available in the market, which must be reduced to ensure that the technology is affordable for all. The high expense of creating and sustaining intelligent machines could also be seen as a technological limitation of AI, especially because AI is continually changing, necessitating gradual updates to hardware and software to comply with the latest modern specifications. Machines need costly repairs and upkeep. Due to the complexity of the devices, the creation needs significant costs (Ben-Ayed et al., 2013).

High infrastructure cost is another barrier affecting the adoption of AI in agriculture. It not only includes the initial investment cost but also includes the ongoing maintenance and updating costs. The mitigation strategies include the provision of subsidies, grants, and training provided by the government help to reduce the financial burden for farmers. One such example is India's Digital Agriculture mission (Gardezi et al., 2024). On the other hand, collaborative approaches and the use of open-source platforms such as TensorFlow and PyTorch for crop disease detection, soil health monitoring, and weather forecasting (Lohith et al., 2022; Pechuho et al., 2020).

A platform built on open source would reduce the price of the solutions, permitting them to be adopted quickly and to reach more farmers (Talaviya et al., 2020). Many physical devices, including IoT, are initially susceptible to assaults on the hardware since they are sometimes left unattended in an open area for a long time. The usage of blocker tags, frequency-modified tags, data encryption, and tag deletion policies are examples of examples of common precautions for safety. Services that use location are also vulnerable to device capture attacks, which implies that after capturing the target device, the attacker may recover encrypted algorithms and afterwards have unrestricted access to the data on the target device (Zha, 2020). The majority of AI systems based on the internet, their use is limited, particularly in isolated or rural locations. The government can benefit farmers by developing a web service-enabled device with a lower tariff that can integrate with their AI systems. Additionally, training and retraining in “how to use” techniques will greatly assist farmers in adjusting to the usage of AI on their farms (Eli-Chukwu, 2019).

11. Conclusion

Monitoring agriculture is essential to minimising human intervention in daily life. The demand for food is increasing constantly, and without the deployment of modern and high-tech agricultural technologies, it will be very challenging to meet this demand. AI has been used to assist farmers with fertilizer selection and crop selection. The machine communicates with one another to determine which crop is fit for harvesting and the fertilizers which stimulate the greatest growth with the aid of the database that the user has compiled and provided to the system. By enhancing the qualities of agricultural products, AI is revolutionizing agriculture in profound ways. DL is widely applicable, and its use in agriculture has advanced significantly. IoT has made a significant contribution to aiding in data monitoring in real-time. Smart irrigation, yield forecasting, post-harvest management, and on-farm sorting are just a few of the AI-powered applications that are boosting productivity, decreasing waste, and boosting crop yields. Furthermore, AI is enabling the identification of diseases and pests, the targeted application of pesticides, and the monitoring of soil health, resulting in sustainable and productive agricultural practices. As AI develops, it has a lot of potential to address issues with global food security and sustainability by enhancing the productivity, resilience, and efficiency of agriculture. It is critical to use technological advances at various points along the agricultural supply chain, including the automation of farm equipment, the use of sensors and satellite data from a distance, AI, ML, and water for improved crop monitoring. These factors include climate change, population growth, technological advancement, and the state of natural resources.

CRediT authorship contribution statement

Mansi Nautiyal: Validation, Conceptualization, Writing – review & editing, Methodology, Writing – original draft, Data curation. Saloni Joshi: Writing – review & editing, Conceptualization, Validation, Data curation. Iqbal Hussain: Writing – review & editing, Methodology. Hrithik Rawat: Methodology, Writing – original draft, Validation. Akanksha Joshi: Methodology, Writing – original draft, Validation. Aditi Saini: Data curation, Writing – review & editing. Rishiraj Kapoor: Writing – review & editing, Methodology. Himani Verma: Writing – review & editing, Methodology. Anshul Nautiyal: Validation, Writing – review & editing. Aniket Chikara: Methodology, Data curation. Waseem Ahmad: Writing – review & editing, Validation, Methodology. Sanjay Kumar: Writing – review & editing, Validation, Conceptualization.

Consent to publish

Not Applicable.

Ethical approval and consent to participate

Not applicable.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.