Severity Stage Identification and Pest Detection of Tomato Disease Using Deep Learning

In Bangladesh, most people depend on agriculture for their livelihood. The country's economy and agricultural production are hampered if plants affected by diseases. Crop pests also disrupt production. So, this paper proposes leaf disease, disease severity stage, pest detection strategies with suggestions prevention using five notable Convolutional Neural Network models (CNN) such as VGG16, Resnet50, AlexNet, EfficientNetB2, EfficientNetB3. This uses a dataset of tomato leaves consisting 41,763 images which cover 10 classes 4,271 8 pests. Firstly, these used the classification diseases Then techniques shown. For detection, EfficientNetB3 gives best accuracy training (99.85%), (99.80%), validation (97.85%), (97.45%) respectively. stage identification, AlexNet (69.02%) (72.49%).