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

Abstract

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