Table 2.
Automation and AI Tools in plant monitoring.
| Platform | Automation Technology | Imaging Device | Phenotype/Parameter | Plant Species | References |
|---|---|---|---|---|---|
| UAV remote sensing | Multirotor UAV with CNN architecture | XIMEAMQ022MG-CM Camerawith CMOS sensor and 16 mm lens and Sony NEX-7 Camera | Disease severity at 25m altitude | O. sativa (rice) | Bai et al. (2023a) |
| High throughput UAV remote sensing | DJI Phantom 4 Advanced quadcopter | Drone RGB camera | Accurate plant count, location and size determination to distinguish in paddy field at 7m altitude | O. sativa(rice) | Bai et al. (2023b) |
| RiceNet | Deep Learning Network | ||||
| Edge-computing based network monitoring | IoT monitoring with deep learning algorithm-based Edge Image Processing Architecture | Raspberry Pi Camera with 5MP sensor |
• Plant growth • Environment and Water quality |
- | Wan et al. (2022) |
| GrowBot | Robotic system with U-Net: CNN | OV5647 CMOS image sensor with Raspberry Pi4 | Plant growth based on nutrient deficiency and temperature stress |
Ocimum basilicum
(basil) |
Bose and Hautop Lund (2022) |
| AscTec Navigator 3.4.5 | UAV with built-in GPS | AscTec Falcon 8 octocopter (Ascending technologies, Germany) Sony α6000 24.3 MP camera with 20mm f/2.8 lens |
• Leaf Area Index at 20m altitude • Leaf/biomass growth • Vegetation indices • Chlorophyll index |
A. hypogaea L. (peanut) |
Sarkar et al. (2021) |
| WEKA (Waikato Environment for Knowledge Analysis) software v3.8.4 | ANN | ||||
| WOFOST | UAV imaging integration | - | Leaf area index (LAI), biomass, yield |
T. aestivum
(winter wheat) |
Yang et al. (2021a) |
| Hyperspectral Reflectance | MLP, SVM and RF with remote sensing | UniSpec-DC Spectral Analysis System (PP Systems International Inc., USA) |
• Biomass yield • Plant growth and development stages |
G. max
(soybean) |
Yoosefzadeh-Najafabadi et al. (2021) |
| Greenotyper | U-Net: CNNs | RPi3 Model B with RPi Camera module v2.1 |
• Plant area • Greenness • Overlapping growth patterns |
Trifolium repens
(white clover) |
Tausen et al. (2020) |
| Keras | U-Net based CNN segmentation model | 2592 x 1944 x 3 resolution camera (5 MP) | Powdery mildew disease detection |
Cucumis sativus
(cucumber) |
Lin et al. (2019) |
| CropDeep | RetNet with ResNet50 CNN | IoT cameras, Autonomous Spray robots, Autonomous Picking Robots, Mobicamera and Smartphone camera |
• Precision farming • Plant identification, growth and location • Different plant variety monitoring • Fruit and vegetable health status |
25 plant varieties including L. sativa Linn. (lettuce), A. graveliens Linn. (celery), Cucumis Linn. (cucumber), B. oleracea Linn. (cabbage), S. oleracea Linn. (spinach), L. esculentum Mill. (tomato), R. sativus Linn. (turnip) | Zheng et al. (2019) |
| Alexnet | CNN-Long-Short Term Memories (LSTM) architecture | Canon EOS 650D | Plant growth pattern of different genotypes | A. thaliana | Taghavi Namin et al. (2018) |
| Persistent Homology based topological methods | DIRT (Digital Imaging of Root Traits) Gaussian kernel density estimator Elliptical Fourier descriptors |
- |
• Leaf shape, serrations and root architecture • Discrimination between genotypes |
Solanum pennellii
(wild tomato) |
Li et al. (2018) |
| PlantCV | U-Net based CNN | Raspberry Pi Camera | Plant convex hull, width and length | A. thaliana | Tovar et al. (2018) |
| Nikon COOLPIX L830 Camera | Seed size, shape, count and color | Chenopodium quinoa Willd. (Quinoa) | |||
| LeafNet | Caffe framework based Deep Learning CNN | LeafSnap, Flavia and Foliage dataset images using Mobile cameras (iPhones mostly) | Species identification through leaf features like edges and venations | LeafSnap, Flavia and Foliage dataset | Barré et al. (2017) |
| Deep Plant Phenomics (DPP) | Deep CNN with PlantCV module | Canon PowerShot SD1000 7 MP camera, Model B with Raspberry Pi 5 MP camera module | Leaf size, shape and leaf count |
A. thaliana
N. tabacum (tobacco) |
Ubbens and Stavness (2017)
Minervini et al. (2015) |
| phenoSeeder | KR 10 scara R600-Z300 robot (KUKA Roboter GmbH, Germany) | Oscar F-810C Camera (Allied-Vision Technologies, GmbH, Germany) | Seed projected area, length, width and color | B. napus (rapeseed), H. vulgare (barley) and A. thaliana | Jahnke et al. (2016) |
| Grasshopper GRAS-50S5M-C Camera (Point Grey, Canada) with 35mm lens | Seed volume | ||||
| UAV remote sensing SAMPLINGTSPN |
UAV and GPML (Gausian Processes for Machine Learning) Toolbox |
MikroKopter, Hexa XL with Multispectral Tetracam Camera | Nitrogen level prediction at 30m altitude | Z. mays (maize) | Tokekar et al. (2016) |
| DIRT (Digital Imaging of Root Traits) | - | - | Root angles (top and bottom), stem diameter, width of root system | Z. mays (maize) | Das et al. (2015) |
| GARNICS | Robotic system with ML-based algorithms | Robot head with 4 x Point Grey Grasshopper, 3.45 μm pixels Camera and Schneider KreuznachXenoplan 1.4/17-0903 lenses Canon PowerShot SD1000 7 MP camera, Model B with Raspberry Pi 5 MP camera module |
• Plant detection and localization • Plant and leaf segmentation • Leaf shade, appearance and difference detection • Leaf counting • Leaf growth tracking • Classification based on mutant and treatment recognition and age regression |
A. thaliana
N. tabacum (tobacco) |
Minervini et al. (2015) |