Image Processing of Big Data for Plant Diseases of Four Different Plant Categories

Abstract

In this research, plant pathogens are considered as big data because of the numerical counts for high intensity pixels in the images. The research presents an automated approach for early detection of plant diseases using image processing techniques. By analyzing the color features of leaf areas, the k-means algorithm for color segmentation and the Gray-Level Co-Occurrence Matrix (GLCM) are used for disease classification. A novelty of this research is that it illustrates four categories of plants to analyze and compare: (1.) Grain, represented by Rice Plant Leaf Data; (2.) Fruit, represented by banana plant leaf data, (3.) Flower, represented by sunflower plant leaf data; and (4.) Vegetable, represented by potato plant leaf data. Six stages of image processing are applied to real data for diseases of leaf smut for rice, black sigatoka for banana, leaf scars for sunflower, and late blight for potato. Finally, a comparison of the image processing for each of the four plant types, conclusions, and future research directions are presented.

Plant phenotyping relevance

葉画像から植物病害を自動検出・分類する画像処理手法が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として含める。

Code and data availability

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