End-to-End Semantic Leaf Segmentation Framework for Plants Disease Classification

Abstract

Pernicious insects and plant diseases threaten the food science and agriculture sector. Therefore, diagnosis and detection of such diseases are essential. Plant disease detection and classification is a much-developed research area due to enormous development in machine learning (ML). Over the last ten years, computer vision researchers proposed different algorithms for plant disease identification using ML. This paper proposes an end-to-end semantic leaf segmentation model for plant disease identification. Our model uses a deep convolutional neural network based on semantic segmentation (SS). The proposed algorithm highlights diseased and healthy parts and allows the classification of ten different diseases affecting a specific plant leaf. The model successfully highlights the foreground (leaf) and background (nonleaf) regions through SS, identifying regions as healthy and diseased parts. As the semantic label is provided by the proposed method for each pixel, the information about how much area of a specific leaf is affected due to a disease is also estimated. We use tomato plant leaves as a test case in our work. We test the proposed CNN-based model on the publicly available database, PlantVillage. Along with PlantVillage, we also collected a dataset of twenty thousand images and tested our framework on it. Our proposed model obtained an average accuracy of 97.6%, which shows substantial improvement in performance on the same dataset compared to previous results.