A Study on Crop Disease Detection of Banana Plant using Python and Machine Learning

Abstract

Crop or leaf disease detection using Python and Machine learning application is designed by using image processing technique for the purpose of farmers to identify, analyze and classify automatically through the computer vision and machine learning vision system for mainly banana leaf to find diseases and by plotting the graph for their pixel range of the affected areas. Leaf diseases are restricting the growth of the plants and it is also destroying the crop. Disease can be controlled by knowing which disease is destroying the plant. The symptom of the banana diseases will be noticed in the leaf, by change in color to yellowish and turning to a dark color and this can be observed between the fourth and fifth month of the plant. Causing reduction in the growth of the plant as well as rotting of the banana. The support vector machine (SVM) algorithm is used for extraction of color and texture features. The proposed work attains a high accuracy in identification of diseases and thereby controlling the spread in other plants.

Plant phenotyping relevance

バナナ葉の画像から病徴・罹病状態を抽出し、色・テクスチャ特徴と機械学習で病害を識別する方法が研究の中心であり、植物病害状態の表現型計測に該当する。

Code and data availability

The paper describes banana leaf disease detection using SVM and image processing on ~250 self-collected leaf images, but contains no data availability statement, no public dataset or code deposit, no repository identifier, and no author-provided URL for any asset. The leaf images, histograms, and results are only shown

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