IoT Enabled Smart Agriculture System for Detection and Classification of Tomato and Brinjal Plant Leaves Disease

Abstract

Internet of Things (IoT) assisted smart farming techniques are gradually being used efficiently for identification and classification of vegetable plant diseases. Detection and classification of diseases in these plant families like Solanaceae are still problematic using DCNN due to variations in environmental conditions, genome variation, type of disease, etc. In this paper, two methods for spotting and diagnosing diseases of brinjal and tomato plants leaves named as Optimal Environmental Traversing Alert (OETA) and Optimum diagnosis of Solanaceae leaf diseases (ODSLD) respectively have been proposed. The OETA machine learning (ML) based method is used first to detect the disease, and then the ODSLD deep convolutional neural networks (DCNN) method is used to classify it. An analysis of the proposed method experiments showed that OETA disease detection for brinjal plant (eggplants) was 97.81 percent and for tomato plants was 99.03 percent. For disease classification by ODSLD method, the VGG-16 for brinjal plant and ResNet-50 for tomato plants outperformed other existing DCNN computer vision methods.