InsightNet: A Deep Learning Framework for Enhanced Plant Disease Detection and Explainable Insights

Abstract

Sustainable agriculture holds the key in meeting food production requirements for a rapidly growing population without exacerbating environmental degradation. Plant leaf diseases pose a critical threat to crop yield and quality. Existing inspection methods are labor-intensive and prone to human errors, while lacking support for large-scale agriculture. This research aims to enhance plant health by developing advanced deep learning models for the detection and classification of plant diseases across a variety of species. A deep learning model based on the paradigm of the MobileNet architecture is proposed, which employs a dedicated design through deeper convolutional layers, dropout regularization, and fully connected layers. This results in significant improvements in disease classification in tomato, bean, and chili plants, with accuracy rates of 97.90%, 98.12%, and 97.95%, respectively. Moreover, Grad-CAM is used to shed light on the decision-making process of the proposed model. The work contributes to the advancement of precision farming and sustainable agricultural practices, supporting timely and accurate plant disease diagnosis.

Plant phenotyping relevance

植物葉の病害状態を画像から検出・分類する深層学習法の開発と評価が研究の中心であり、植物フェノタイピング手法に該当します。

Code and data availability

The supplied blocks describe three plant disease image datasets (chili, tomato, bean) and the InsightNet model, but contain no public deposit URL, repository, accession, or code/data availability statement. The datasets' provenance is not linked to any public source, and no authors' code or trained model checkpoint is,

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