Identification And Diagnoses of Plant Diseases in Fruit Crops Using Machine Learning Algorithms

Abstract

India, being an agriculture-centric nation, historically relied on traditional farming methods that often resulted in crop losses and significant financial setbacks for farmers. In the present era, however, the incorporation of technology in agriculture has led to a rise in crop value, still, there is a great scope of research. One of the key reasons of the production of low-quality crops is the presence of infections or diseases. This research paper focuses on the application of machine learning algorithms, specifically Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and MobileNetV2, for plant disease detection in apple, strawberry, and grape leaves. The dataset used comprises 29,327 images, and various performance metrics were employed to evaluate the models' accuracies. According to the findings, the SVM model performs best with 82%, 98% and 72% accuracy for apple, strawberry, and grape crop respectively. Also, the precision, recall and F-score values are significantly high for CNN. So, among three considered model in the experiment for identification of disease in apple while for grapes and strawberry CNN works better than other two models.

Plant phenotyping relevance

葉画像から植物病害を推定する機械学習手法を比較・評価しており、植物の病徴状態の取得・判定が研究の中心です。

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