Vision based Cassava Plant Leaf Disease Classification using Machine Learning Techniques

Abstract

Plant leaf infection recognition using supervised machine learning has emerged as a promising solution to address the pressing challenges in agriculture and plant pathology. Firstly, a wide-ranging dataset of real-time cassava images, comprising both infected and healthy plant leaves, is methodically amassed and accurately annotated. The real time dataset covered healthy cassava leaves, Cassava Bacterial Blight diseases, Cassava Brown Streak Disease diseases, Cassava Green Mottle diseases, Cassava Green Mottle diseases and Cassava Mosaic Disease. The data preprocessing phase involves an assessment of filtering, noise reduction, resizing, and extraction techniques to maintain uniformity and augment the dataset’s variability. Following this, relevant details are extracted from the preprocessed cassava frames, which are then utilized as inputs for the machine learning model. The approach of combining Leaf Intensity Vector, Principle Component Analysis, Gray Level Co-occurrence Matrix, and Support Vector Machine is adopted for the recognition of cassava leaf diseases. Using the polynomial and RBF kernel of SVM, KNN, Random Forests, and Decision Trees, the extracted suggested features are finally categorized. The proposed cassava leaf diseases classification system performs well, providing greater SVM RBF accuracy (98.4%) on models with disordered cassava leaves.

Plant phenotyping relevance

カッサバ葉の画像から病徴・病害状態を抽出し、特徴量と機械学習による分類手法を開発・評価しており、植物フェノタイピング手法が中心である。

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