A Deep Learning model for the identification of Potato leaf diseases using Wrapper Feature Selection and Concatenation

Abstract

Abstract Potato is a popular crop that is cultivated in many different climates. Potato farming has recently gained incredible traction, increasing relevance in international agricultural production. Potatoes are susceptible to several illnesses that stunt their development. This plant has significant leaf disease. Early blight (EB) and late blight (LB) are the two devastating leaf diseases for potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is image processing to identify and analyze these disorders. Using image processing and machine learning, we detail a method that requires no outside help to detect late-blight potato leaf in this article. The pro- posed method comprises four different phases: (1) Histogram input images may improve from equalization to boost their overall quality; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. By utilizing SVM and a meticulously selected set of 550 characteristics, the suggested technique achieves an unprecedented 99% accuracy.

Plant phenotyping relevance

ジャガイモ葉の病害状態を画像から推定する深層学習・特徴選択・分類手法が研究の中心であり、植物病害表現型の取得・推定に該当する。

Code and data availability

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