Edge-Based IoT Plant Leaf Condition Classification Using Classical Digital Image Processing on Raspberry Pi

Abstract

This study aims to develop an edge-based Internet of Things (IoT) system for automatic plant leaf condition classification using classical digital image processing on a Raspberry Pi. The proposed system classifies leaf conditions into healthy, diseased, and pest-attacked categories while providing real-time remote monitoring through a Telegram Bot. The system employs a Raspberry Pi as the edge computing device and a Raspberry Pi Camera for image acquisition. Images are processed locally using OpenCV through RGB-to-HSV color space conversion, thresholding, edge detection, and contour analysis. System performance was evaluated using 15 test samples for each image acquisition distance (15 cm, 30 cm, 45 cm, and 60 cm). Experimental results achieved detection accuracies of 100% at image acquisition distances of 15 cm and 30 cm, while the accuracy decreased to 87% at 45 cm. At 60 cm, the system failed to detect the target object because insufficient visual information prevented reliable feature extraction. The proposed edge-based IoT system provides an efficient and low-cost solution for real-time plant leaf condition classification. The experimental results indicate that an image acquisition distance of 15-30 cm is optimal for reliable detection under the evaluated experimental conditions. The proposed system integrates Raspberry Pi-based edge computing with lightweight classical digital image processing and Telegram Bot notifications, eliminating the need for computationally intensive deep learning models or cloud-based image processing.

Plant phenotyping relevance

植物葉の健全・病害・害虫被害状態を画像から分類する手法と、Raspberry Pi上の実装・性能評価が研究の中心であり、植物状態のフェノタイピング手法に該当する。

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