## 4 Data Extraction and Analysis
\begin{table}
\begin{tabular}{l l l l} \hline
**Paper** & **Dataset** & **Algorithm** & **Accuracy** \\ \hline
[37] & Self-collected dataset & Faster R-CNN, Mask R-CNN & 31\% to 96\% \\
[38] & Self-collected dataset (3.900 images) & MIB Classifier & 98\% \\
[39] & From [PERSON]’s website & Five-layered CNN model & 99.99\% \\
[40] & PlantVillage & Own CNN model with 18 layers and 28 & 99.12\% \\
[41] & Self-collected dataset & Cascade R-CNN model with Res2 Net & 81.0\% (mAP) \\
[42] & Self-collected dataset & CNN models (VGG16 \& VGG19) & VGG16 (98.52\%) and VGG19 \\
[43] & Self-collected dataset (50k images) & CNN + k-NN Classifier + SVM Classifier + Random Forest Classifier & 99.2\% \\
[44] & Self-collected dataset (brinjal leaf & ANN Classifiers & 91\% (with FFNN)) and 98.1\% (with CFNN) \\
[45] & PlantVillage & MobileNet-V2, NASNetMobile, & 98.44\% and 99.46\% \\  & & EfficientNet-B0, and DenseNet-121) & \\
[46] & Self-collected dataset & Transfer Learning model & 99.86\% \\
[47] & PlantVillage & EfficientNet families & EfficientNet-B4) \\
[48] & Self-collected dataset & Faster R-CNN & 0.944 and 0.952 \\
[49] & CIFAR-10 dataset & CNN model with 1 input layer & 94.45\% \\
[50] & Self-collected dataset & ORB algorithm & 91.89\% \\
[51] & PlantPathology & VGG16 DL model & 85\% \\
[52] & N/A & CNN model & More than 90\% \\
[53] & Self-collected dataset & Models: MobileNetv2 and DenseNet201 & 95.7\% \\
[54] & PlantVillage & Multi-Layer CNN & 94.55\% \\
[55] & PlantVillage and Coffee Leaf dataset & ResNet18, ResNet34 e ResNet50 (models from CNN) & 99\% and 81\% \\
[56] & PlantVillage & ML classifiers (Random Forest, k-NN, Decision Trees, SVM, and others) & 97\% (higher precision from RF) \\
[57] & PlantVillage & FSL method, SVM classifier & 94.0\% (higher precision) \\
[58] & Kaggle datasets & SVM, LR, RF & 87.6, 67.3 and 70.05 \\
[59] & N/A & K-Means Clustering (for alerts) & 95.1613\% \\
[60] & ImageNet and PlantVillage & VGGNet & 91.82\% and 92.0\% \\
[61] & Images taken from UAV (drone) & DL models (ResNet-50, Xception, VGG-19 and Inception-v3) & 99.04\% \\
[62] & PlantVillage & Enhanced Fusion Fractal Texture Analysis & 97.69\% \\
[63] & Self-collected dataset & Faster R-CNN models & 95.48\% \\
[64] & Kaggle datasets & SVM + ANN & SVM (93.12\%) and ANN (88.96) \\
[65] & NBAIR dataset, Xie1 and Xie2 dataset & Self-model proposed is based on CNN & 96.75, 97.47, and 95.97\% \\
[66] & Plant health (7176 images of tomatoes used) & CNN (MobileNet Model) & 90.3\% \\
[67] & Self-collected dataset & 11 layers-based D-CNN model & 98.74\% \\
[67] & (BU\_Guava\_Leaf (BUGL2018)) & SVM classifier & 98.46\% \\
[68] & Self-collected dataset (images taken by digital camera) & SVM, adaboost, and Random Forest tree & 93\% \\
[69] & PlantVillage & AlexNet, GoogleNet and LeNet & 94\%–95\% \\
[70] & PlantVillage & RSC (Random Subspace Classifier) and & 88\% (CPB with LIRA) and 89\% \\
[71] & Self-collected dataset & LIRA (Limited Receptive Area) & (MBB with RSC) \\
[72] & Self-collected dataset & VGG16 + SVM & 89\% \\
[73] & Heilongjiang Academy of Land & CNN model & 95.48\% \\  & Reclamation Sciences, China & & \\
[74] & Self-collected dataset (Images from & SVM Classifier & 95.71\% \\
[75] & Middlebury dataset & SVM classifier & More than 90\% \\
[76] & Self-collected dataset & CNN (ResNet50 and DenseNet121) & ResNet50 (97.59\%) and DenseNet121 (90.50\%) \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._