Plant Disease Identification Using Convolutional Neural Network and Transfer Learning

Abstract

Abstract: Agriculture is an important part of our economy and has attracted our attention since the Middle Ages. India's population is mainly dependent on agriculture, accounting for 60~70%. Global crop losses from a variety of reasons, including weeds, disease, and arthropods, have increased at an alarming rate, from about 34.9% in 1965 to about 42.1% in the late 1990s. Bacteria and fungi can cause many diseases in plants. Many diseases such as Early blight and late blight are fungi that afflict plants. In our research, we provide CNN models and algorithms for detecting leaf diseases in crops. This study discusses the feasibility of CNNs and Transfer Learning for classifying plant diseases. This model is built using a basic CNN architecture to classify potato diseases. From the Plant Village database, 2,152 samples containing photos of leaves in three classes with images of healthy leaves were obtained and used for initial training to check the feasibility of plain CNN and then dataset with 54306 plant images was used to train the bigger plain CNN and Resnet152v2 and Inceptionv3 Architecture for detecting plant diseases using Transfer Learning. The photos were taken in an unstructured environment. The constructed model obtained a classification accuracy of 97.57%, clearly demonstrating the feasibility of utilizing CNNs to classify plant diseases.

Plant phenotyping relevance

植物葉の画像から病害状態をCNNで推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper's plant-phenotyping inputs are entirely the public PlantVillage leaf-image dataset (2,152 potato leaf images and 54,306 images across 38 classes), which the authors state they downloaded; no author code, models, or supplementary deposits are mentioned.