## 4 Data Extraction and Analysis
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Paper** & **Year** & **Application** &
\begin{tabular}{l} **Disease** \\ **or Pest** \\ \end{tabular} & **Crop** & **Disease or Pest Type Found** & **Input** \\ \hline
[55] & 2020 & Algorithm & Disease & Coffee & N/A & Leaf images \\
[56] & 2020 & Algorithm & Disease & Potato & N/A & Leaf images \\  & & & & Cherry, Corn, & \\  & & & & Grape, Orange, & \\
[57] & 2020 & Algorithm & Disease & Peach, Pepper, & Several diseases & Leaf images \\  & & & & Potato, Raspberry, & \\  & & & and Soybean & & \\
[58] & 2020 & Algorithm & Disease & Tomato & N/A & Leaf image \\
[59] & 2020 & Algorithm & Disease & N/A & Alternaria alternata & Leaf image \\
[60] & 2020 & Algorithm & Disease & Rice & Stackburn, Leaf Scald, Leaf Smut, White & Leaf images \\  & & & & Tip, and Bacterial Leaf Streak. & Leaf images \\
[61] & 2020 & Algorithm & Disease & Soybean & N/A & Leaf images \\  & & & & Peanut, Paddy, & \\
[62] & 2020 & Algorithm & Disease & Cotton & \\
[63] & 2019 & Algorithm & Disease & Sugar beet & Cercospora beticola Sac. & Leaf image \\
[64] & 2019 & Algorithm & Disease & Citrus & and others. & Leaf images \\
[65] & 2019 & Algorithm & Pest & N/A & Several insects & Insects’ images \\
[66] & 2019 & Mobile & Disease & Tomato & Bacterial spot Xanthomonas campestris, & Leaf imagens \\
[67] & 2019 & Algorithm & Disease & Guava & Whitefly, Algal leaf spot, and Rust & Leaf images \\  & & & & Bacterial Blight, Alternaria Leaf Spot, & \\
[68] & 2019 & Algorithm & Disease & Cotton & Gray Mildew, Magnesium Deficiency, & Leaf images \\  & & & & and others & \\
[69] & 2019 & Algorithm & Disease & Grape & Black rot, Esca, and leaf blight & Leaf images \\
[70] & 2018 & Algorithm & Disease & Tomato & Septoria leaf spot, Yellow leaf curl & Leaf images \\
[71] & 2018 & Algorithm & Pest & Bean, Potato & Mexican Bean Beetle and Colorado & Fruit images \\
[72] & 2017 & Algorithm &
\begin{tabular}{l} Disease \\ and pest \\ \end{tabular} & Tomato & Several tomato diseases & Leaf images \\
[73] & 2017 & Algorithm & Disease & Rice & Several diseases & Leaf images \\
[74] & 2017 & Algorithm & Disease & Bens, Lemon & Several diseases & Leaf images \\
[75] & 2015 & Algorithm & Disease & Tomato & Powdery mildew fungus, Oidium & Leaf images \\
[76] & 2021 & Algorithm & Disease & Peanut & Rust, scorch, and leaf-spot & Leaf images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l l l} \hline \hline
**Paper** & **Dataset** & **Algorithm** & **Accuracy** \\ \hline
[29] & Self-collected dataset & Faster R-CNN & 93.2\% \\
[30] & Self-collected dataset (20k images) & EfficientNet-b0 model from CNN & 97\% or more \\
[31] & N/A & YOLOv4 & 97.55\% \\
[32] & Dataset from paddy farmlands & VGG-16 and GoogleNet CNN models & 92.24\% and 91.28\% \\
[33] & Self-collected dataset (12k images) & CNN (VGG 16 + ToLCNDV and ToLCGV) & 97.211\% \\
[34] & [PERSON]’s website—with 10.239 leaf & k-NN approach (LBP, HOG, TEM, GWT, and GLCM) & 81.55\%–89.7\% \\
[35] & PlantDoc (2567 images) & Generative Adversarial Networks (GAN modules) & 51.7\% (Precision), 48.1\% (Recall), and 50.3\% (mAP) \\
[36] & Self-collected Dataset & Fitness-distance balance deep neural network (FDB-DNN) & 2.8140\%, 1.507\%, 5.125\%, and 0.402\% higher than the existing state-of-art \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the datasets and algorithms.