Harnessing Machine Learning for Predictive Analysis of Crop Resistance to Extreme Weather Conditions

Given the increasing pressure of extreme weather conditions threatening agricultural productivity, it is becoming more and more important to rely on advanced machine learning techniques to predict this type of unpredictable events. In this paper, we deal with the integration of Long Range Wide Area Network (LoRaWAN) technology with high level machine learning models for forecasting crop resistance to severe weather incidents. With its use in LoRaWAN, remote fields quickly collect real time weather and crop health data and a complete dataset is generated for analysis. Weather and crop responses are captured and also forecasted with better accuracy using Long Short Term Memory networks and Sequence to Sequence models. To fine tune model hyperparameters in a way of optimizing performance and computational efficiency, Bayesian Optimization is used. Adaptive decision making strategies are developed using deep Q-Networks (DQN) which enable adaptivity with respect to changes in environment conditions. The results show that including mean value and mean structure achieves performance in terms of reduced error (mean squared error (MSE) reduced by 15%) and incorporates index (WAI) Down 12%)). In addition the time needed in model training was reduced by 20%) due to intelligently tuned hyper parameters and by 30% decrease in number of iterations during optimization contributed to an increased responsiveness and accuracy of predictions. It yields insights that could be used for supporting actions that help farmers and agricultural planners make decisions that improve crop resilience and make agriculture sustainable against a backdrop of climate change.