An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

Abstract

One of the most important crops for economic survival is cotton, and one of the biggest challenges it faces is early disease detection that affect productivity. The cotton business may suffer financial losses because of the frequently insufficient visual detection of these diseases by humans. This study presents an intelligent approach for the detection of cotton plant diseases using Convolutional Neural Networks (CNNs) with a focus on ResNet-152V2 architecture. Leveraging deep learning techniques, specifically ResNet-152V2, the model exhibits robust performance in identifying various diseases affecting cotton plants. The research involved training the model on a diverse dataset encompassing different cotton leaf diseases. Results demonstrate a better accuracy, with the proposed approach achieving an impressive precision in disease detection. The utilization of ResNet-152V2 enhances the model's capability to accurately classify and diagnose cotton plant diseases, showcasing its efficacy for real-world applications. The study contributes to the advancement of automated disease detection systems in agriculture, particularly in the context of cotton crops.

Plant phenotyping relevance

綿花葉の画像から病害をCNNで自動分類する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。

Code and data availability

The article describes cotton leaf disease detection using CNNs on a cotton leaf dataset, but contains no public dataset deposit, no author code/model release, and no availability statements. Datasets mentioned (e.g., 'cotton leaf dataset, including the normalized-dataset, normalized-augmented dataset') lack any public,

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