A Review of challenges and solution in the detection of disease in the Brinjal leaves

Abstract

Abstract Crop damage and monetary losses occur due to plant diseases. At specified periods, farmers closely monitor their crops in the field to look for infections or diseases. In place of manual diagnosis, computers have been used to provide computerized tracking and recognition of a variety of diseases.This study makes a contribution to denoising algorithms that improve contrast, edge details and picture details. To isolate the leaf affected area, segmentation was carried out. Image fusion is performed using the segmented image obtained from several segmentation techniques, and features are extracted according to structural, texture, and color criteria. Furthermore, an image fusion technique based on Discrete Shearlet Transforms (DST) is applied to improve imaging quality while reducing redundancy. The color, texture, and structural elements of the fused images are retrieved and fed into the Artificial Neural Network (ANN) for classification, resulting in improved performance. When compared to other classification methods (SVM, MSVM, FFNN, and RFNN), the accuracy required for the DST-based fusion approach and the Radial Basis Function Neural Network is relatively high. The classification accuracy was 99.35%, with 89.24% sensitivity, 94.67% specificity, and 90.12% precision.

Plant phenotyping relevance

ナス葉の病害領域を画像から分離・特徴抽出し、病害を分類する画像解析手法が中心であり、植物の病徴状態を直接推定している。

Code and data availability

The article describes brinjal leaf image collection and DST-based fusion/classification experiments, but contains no data availability statement, no public dataset deposit, no author code/workflow URL, and no trained model release. Leaf images and results are only shown as figures/tables within the paper itself.

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