Routing enabled optimization and deep recurrent VGG-16 model for plant disease detection in IoT

Abstract

Agricultural productivity is the major source of the economy of the agrarian nation and is also considered the major source of human activities. It is vital to detect and manage plant diseases to enhance the overall growth and quality of agricultural produce. Modern agriculture increasingly uses IoT and automation tools to monitor and collect valuable information on plant health and disease occurrence. Here, the plant disease is detected using Deep Recurrent Visual Geometry Group-16 (DRVGG-16) in the IoT platform with routing based on the Adam Dung Beetle Optimization (ADBO) algorithm. The DRVGG-16 is the combination of Deep Recurrent Neural Network (DRNN) with Visual Geometry Group-16 (VGG-16) and ADBO is the fusion of Adam optimizer with Dung Beetle Optimization (DBO). Initially, the captured agricultural images are transmitted to the base station via ADBO-optimized routing for plant disease detection. The base station initiates disease detection by applying a bilateral filter to preprocess the input plant leaf images. The preprocessed images are segmented using the DeepLabV3+ model, and features are subsequently derived from the segmented leaf areas. Finally, plant disease detection is performed using the proposed DRVGG-16 model. Furthermore, the performances of the proposed schemes are validated, where ADBO attained distance, delay and energy of 0.343 m, 0.305 ms and 0.375 J. The DRVGG-16 obtained superior results of 91.43 % accuracy, 90.63 % True Positive Rate (TPR), 90.73 % precision, 92.34 % True Negative Rate (TNR) and 90.68 % F1-score. Therefore, the proposed IoT-based framework combining DRVGG-16 and ADBO routing effectively detects plant diseases with high accuracy while optimizing energy and transmission efficiency.

Plant phenotyping relevance

植物葉画像から病害状態を推定する画像解析手法を提案し、セグメンテーションと分類モデルの性能を検証しており、病害フェノタイピング手法が研究の中心である。

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