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. 2023 Nov 27;14:1237695. doi: 10.3389/fpls.2023.1237695

Table 7.

Detection of pests and tree diseases. Prediction and evaluation of orchard production .

Purpose (orchard task) Resources and discussions Performance References
Detection of pests and tree diseases
Infected or diseased trees detection UAV; Faster R-CNN and Mask R-CNN approaches and fusing their outputs SEN=81.67% (Barmpoutis et al., 2019)
Detection of the citrus bacterial canker in disease development stages on Sugar Belle leaves and immature fruit UAV; hyperspectral camera; the neural network Radial Basis Function (RBF) and the K-nearest neighbor (KNN) ACC= 94%-100% (Abdulridha et al., 2019)
Identification of fruit tree pests (Tessaratoma papillosa) UAV; RGB camera; Tiny-YOLOv3 mAP= 38.12%- 95.33% (Chen, C.J. et al., 2021)
Detection of the degree of HLB (huanglongbing) infection on large-scale orchard citrus trees UAV; multispectral camera; stacked autoencoder (SAE) neural network ACC= 99.72% (Deng et al., 2020)
UAV; multispectral camera; autoencoder ACC=97.28%, (Lan et al., 2020)
Detection of diseases in vineyards UAV; multispectral camera; LeNet-5, SegNet – single or combination ACC=78.72%-95.02 (Kerkech et al., 2020)
UAV; RGB camera; LeNet-5 ACC=95.8% (Kerkech et al., 2018)
UAV; RGB camera; CaffeNet NA (Bouroubi et al., 2018)
UAV; multispectral camera; VddNet ACC=93.72 (Kerkech et al., 2020)
Detection of the presence and behavior of the nematode pest in coffee crops UAV; RGB camera; U-Net and PSPNet F1 = 69% (Oliveira et al., 2019)
Detection of black rot on grape leaves UAV; RGB camera; YOLOv3 with SPP module PRE=94.05%, SEN=93.26% (Zhu et al., 2021)
Sick tree detection UAV; RGB camera; different CNNs: Alexnet, Squeezenet, VGG 16; Resnet 50, Densenet 121 ACC=97.6% -99.5% (Nguyen et al., 2021)
Bug detection (Halyomorpha Halys) in an orchard UAV; RGB camera; processing (NN) NA (Sorbelli et al., 2022), (Ichim et al., 2022)
Insect detection, invasive species (Anolis carolinensis) UAV, RGB camera; SSD-based model of DCNN PRE=70% (Aota et al., 2021)
Invasion degree of “Solanum rostratum Dunal” detection UAV; RGB camera; DeepSolanum-Net based on U-Net F1 = 89.85% (Wang et al., 2021)
Prediction and evaluation of orchard production
Method for semantic segmentation and instance segmentation of bayberry fruit. Terrestrial platform; RGB camera; Multi-module convolutional neural network AP = 75.5% -91.3% (Lei et al., 2022)
Accurate monitoring of fruit quantity in apple orchards UAV inside orchard; RGB camera; YOLO v5s AP = 90.39% (Wang S. et al., 2022)
Yield estimates in apple orchards. Detecting apples on individual trees. UAV; RGB camera; R-CNN R2 = 80% - 86% (Apolo-Apolo et al., 2020a)
Detection, counting, and estimation of the size of citrus fruits on individual trees UAV; RGB camera; Faster R-CNN F1 = 89% (Apolo-Apolo et al., 2020b)
Detection and location of longan fruits UAV; RGB camera; MobileNet backbone used to improve YOLOv4 mAP = 54.22 -89.73% (Li D. et al., 2021)
Holly fruits detection and counting UAV; RGB camera; YOLOX DR >99% (Zhang Y. et al., 2022)
Canopy extraction. Detect mango and predict the number on the tree Terrestrial platform; RGB camera; Mango YOLO, Xception, Random Forest R2 = 98% (Koirala et al., 2021)
Detect apple fruit in the orchard Manual images; RGB camera; comparing RetinaNet, Libra-RCNN, Cascade-RCNN, Faster-RCNN, FSAF, HRNet, and ATSS Maximum AP = 94.6% (Biffi et al., 2021)
Longan harvesting UAVs. Branch detection and fruit branch semantic segmentation. UAV; RGB-D camera; YOLOv5s – for detection, and improved DeepLabv3+ (MobileNet v2) for semantic segmentation ACC = 85.50% – 94.52% (Li D. et al., 2022)
Grape detection, instance segmentation RGB camera; Mask R-CNN with ResNet 101 as the backbone F1 = 91% (Santos et al., 2020)
Pear (fruit) detection  RGB camera; YOLO-P F1 = 96.1% (Sun et al., 2023)