IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases.

Abstract

In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops. A custom-curated dataset was developed, comprising over 1,800 images acquired through smartphone cameras and foldscope devices under natural lighting conditions. The proposed CNN model achieved a classification accuracy of over 95%, with a disease quantification accuracy of 90.5%, calculated using pixel-level segmentation of infected regions. Experimental results revealed infection percentages ranging from 0.68% in early-stage cases to 13.98% in severely affected samples, enabling precise disease severity analysis. The framework includes a MATLAB-based graphical user interface (GUI) for real-time visualization of classification results and severity scores. Training convergence was demonstrated with a mini-batch loss reduction from 1.0879 to 0.0094 over 200 iterations, and classification confidence scores exceeding 90% for most disease categories. In addition to software implementation, the model was synthesized for hardware deployment using FPGA, demonstrating less than 5% LUT and 1% register usage for 512 × 512 images, ensuring resource-efficient performance in IoT environments. This work introduces a scalable, field-deployable tool for crop health monitoring, with potential to enhance sustainable farming practices through timely disease management.

Plant phenotyping relevance

イネ・ジャガイモの病害画像を用いて感染領域と病害重症度を定量化するCNN・画像処理・GUI・FPGA実装を開発しており、植物表現型（病害状態）の取得・抽出が研究の中心です。

Code and data availability

The paper describes a custom-curated dataset of 1,800+ smartphone/foldscope images, a MATLAB CNN pipeline, and a GUI, but no supplied block contains any public deposit, repository URL, or availability statement for the dataset, images, code, or trained model. The dataset is described as author-created and manually held

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