An Enhanced Deep Learning approach for crop health monitoring and disease prediction

Abstract

Abstract Global warming and lack of immunity in crops have recently resulted in a significant increase in the spread of agricultural diseases. This leads to large-scale crop destruction, less cultivation, and ultimately financial loss for farmers. Identification and treatment of illnesses have become a big issue because of the fast development in disease diversity and lack of farmer knowledge. This paper investigates the application of deep learning for crop disease prediction using a newly acquired dataset of leaf images from Ghana. The dataset focuses on four major crops: cashew, tomato, cassava, and maize. The paper introduces hybrid deep learning models in terms of various evaluation metrics in identifying healthy and diseased plants based on leaf images. This paper also developed a novel hybrid model for this new dataset. The hybrid model ResNet50 + VGG16 resulted in higher precision and accuracy in its predictions, evidencing strong performance and reliability. This work contributes to the development of accurate and accessible tools for crop disease diagnosis, potentially leading to improved agricultural practices and increased crop yields. Through the integration of newer and advanced deep learning techniques, this research will provide a significant step in the field of agriculture for monitoring crop health disease and prediction.

Plant phenotyping relevance

葉画像から植物の健全・罹病状態を推定する深層学習手法の開発と評価が研究の中心であり、植物病害フェノタイピング手法に該当します。

Code and data availability

The paper uses the publicly available CCMT leaf-image dataset from Ghana, but that dataset is cited prior work (Mensah et al., ref 1), not a paper-specific deposit by these authors. No author analysis code, trained model checkpoints, or data availability statement with a public URL appears in the supplied blocks.

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