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Article Content:-
Abstract
The increasing prevalence of crop diseases such as boll rot and other pathogen infections poses a significant threat to agricultural productivity, particularly in resource-constrained farming environments. This study proposes a Reinforcement Learning-Based Adaptive Auto encoder (RL-AAE) framework for energy-efficient detection of boll rot and related pathogens within Internet of Things (IoT)-enabled agricultural networks. The model integrates an adaptive auto encoder for feature extraction and dimensionality reduction with a reinforcement learning (RL) agent that dynamically optimizes sensing, data transmission, and decision-making processes to minimize energy consumption while maintaining high detection accuracy. The adaptive auto encoder is designed to learn compressed representations of high-dimensional sensor and image data collected from distributed IoT nodes, enabling efficient processing and anomaly detection. The reinforcement learning component continuously interacts with the network environment to select optimal actions, such as adjusting sampling rates, transmission intervals, and node participation, thereby prolonging network lifetime and reducing redundant energy usage. The proposed approach is evaluated using key performance metrics including detection accuracy, energy consumption, network lifetime, and latency. Experimental results demonstrate that the RL-AAE model significantly outperforms traditional machine learning and static IoT detection approaches by achieving higher classification accuracy for boll rot and pathogen detection while reducing energy consumption and extending network operational lifespan. This work highlights the potential of combining deep learning and reinforcement learning techniques to address critical challenges in smart agriculture, providing a scalable and intelligent solution for real-time disease monitoring in IoT-based farming systems.
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