Comprehensive Study of Plant Leaf Disease Detection and Classification using Deep Learning Approach

Abstract

Plant leaf diseases pose a major challenge in the agricultural field, leading to poor crop performance and impacting food safety. Timely identification and accurate classification of diseases can prevent plant damage and ensure sustainable farming practices. This paper demonstrates deep learning (DL) approaches for plant leaf disease detection and classification, focusing on Convolutional Neural Networks (CNN) and hybrid/ensemble learning methods. Various datasets, including PlantVillage, Rice Leaf Disease, Corn Leaf Disease, and Apple Leaf Disease, are used for training and testing the models. CNN-based methods such as VGG16, MobileNet, and CapsNet are employed for automatic feature extraction, providing high accuracy. Hybrid models, combining CNN with other algorithms like RNN or SVM, aim to improve performance by addressing issues such as overfitting. A comparative analysis of existing methods is provided, highlighting their advantages, limitations, and performance metrics. The performance of the models is evaluated based on metrics such as accuracy, precision, recall, and F1-score. This paper aims to assist researchers in understanding computer vision applications for plant leaf disease classification.

Plant phenotyping relevance

植物葉の病害状態を画像から検出・分類する深層学習手法を比較評価しており、病害表現型の取得・推定が中心です。

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