The research presents a transformative approach to paddy farming by integrating IoT, blockchain, and DL for real-time pest detection and farm automation. The achievement of a notable 98.91% accuracy in pest identification underscores the efficacy of DL models and ensemble classifiers of our approach. This innovation serves the dual purpose of protecting crop health and promoting agricultural sustainability. The utilization of blockchain technology guarantees the confidentiality and integrity of data transmission, while the rapid average response time of 1.71 s for pest identification through the integration of the IoT enhances the efficiency of decision-making processes. This research fundamentally redefines the practices of paddy farming, offering the potential for a more resilient and automated agricultural future.

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed....