Detection of Plant Leaf Diseases using Machine Learning Techniques and CNN

Abstract

Agriculture is critical for guaranteeing food security, in turn maintaining the economic stability of a country. Diseases in plant leaves are considerable threat to plant health and productivity. Pathology expert perform manual identification of diseases which is time-consuming, labor-intensive and delayed detection can lead to serious crop loss. Early and accurate detection of diseases has to be done prefer ably. It is challenging to diagnosis diseases as leaves exhibit almost similar color, texture features. The focus is to identify and categorize plant leaf diseases with respect to five plant species apple, corn, grape, potato and tomato taken from PlantVillage dataset. These species are considered with over 25 classes that include healthy and diseased classes. The experiments are conducted by image processing techniques and Machine Learning (ML) models such as Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). All the above techniques are considered for comparison by performance evaluation metrics: accuracy, precision, recall, and F1-score. CNN outperforms here in plant disease detection by obtaining an accuracy of 95.37%, 93.92%, 98.00%, 97.10% and 94.88% with respect to apple, corn, grapes, potato and tomato crop leaves respectively.

Plant phenotyping relevance

植物葉の画像から病害状態を分類する機械学習・CNN手法を比較評価しており、植物病害表現型の取得・推定が中心です。

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