A Multi-Agent Policy Gradient Framework for Weather-Adaptive Crop Protection Using Deep Ensemble Learning

Agricultural productivity is also very responsive to dynamic weathers and pest attacks and this requires intelligent and dynamic prediction systems that can be utilized to effectively protect crops. The research hypothesizes a Multi-Agent Policy Gradient Framework of Weather-Adaptive Crop Protection with Deep Ensemble Learning which combines the predictive power of hybrid deep models with the decision-making power of reinforcement learning. The framework uses a multi-agent design, with individual agents focused on checking and forecasting individual environmental indicators, including temperature, rainfall, and humidity, and pest activity, with the help of a complex of convolutional and recurrent neural networks. The ensemble method improves strength and precision by combining various outputs of the models, thus reducing the uncertainty of individual models. Reinforcement learning module is a policy gradient that optimizes the adaptive decision strategies enabling agents to adaptively change their prediction policies as a result of changing environmental patterns. The system learns to determine the best responses to the bad weather conditions and the potential occurrence of a pest through constant feedback means, to assist in taking timely action on crop protection. The framework proposed is validated through multi-source data which incorporates meteorological, satellite and agricultural field data. It has been observed through experimentation that predictive accuracy is greater, adaptation to climate variations is more rapid and predictive outcomes such as crop yields are more resilient when using experimental models rather than the traditional static ones. The present research augments the methodology of scaling, smart, real-time weather-adaptive crop protection, which is in line with the goal of sustainable agriculture and precision farming in unpredictable climatic scenarios.