Table 5.

Tomato vegetable classification using AI.

References	AI methods	Dataset	Disease	Accuracy
(Francis and Deisy, 2019)	CNN	PlantVillage	Leaf spot and mosaic virus	CNN=87%
(Basavaiah and Anthony, 2020)	RF and DT	PlantVillage	Bacterial, Septoria, spider mite, target spot, and healthy	RF=94%
(K and Rao, 2019)	KNN and PNN	Self-collected	Miners, verticillium wilt, spider mites, and powdery mildew	KNN=91.88%
(Vadivel and Suguna, 2022)	BPNN, NN, CNN, SVM and RBF	PlantVillage	Bacterial spot, mosaic, Septoria, and yellow curl	CNN=99.4%
(Chakravarthy and Raman, 2020)	RestNet and Xception	PlantVillage	Early blight	99.952%
(Kumar and Vani, 2019)	CNN	PlantVillage	Target spot, mosaic, septoria, and leaf mould	CNN=99.25%
(Kumari et al., 2019)	CNN	PlantVillage	Septoria leaf spot and leaf mold	CNN=100%
(Govardhan and Veena, 2019)	Random Forest	PlantVillage	Blight, both early and late, septoria leaf spot, spider mite, and mosaic	RF=95.2%