Design and Development of An Automated IoT-Aided Smart Agriculture Management System for Efficient Crop Yield Prediction

Crop yield forecasting is a fundamental part of current precision agriculture which enables sustainable farming practice and optimal resources scheduling. Combining IoT with deep learning can improve predictive performance by benefiting from the real-time data gathering and the power of computational models. In this study, an IoT-based crop production forecasting framework is introduced which uses the LEO, during the progress of the system to effectively route the data, so that the energy consumption will be minimum and the data transmitted is precise. The collected data is pre-processed and features are extracted by a Temporal Convolutional Network (TCN) to capture long-range dependencies of agricultural-related data. These representations are passed to a hybrid BiLSTM-WANN network which integrates a BiLSTM to capture the time dependency followed by a WANN network that optimizes the model structure without weight updates. What is more, we also propose to LEO to update weight parameters in order to reduce prediction error and increase accuracy. This implication results in an efficient, scalable, and high-performance architecture for crop yield prediction. The performance of the proposed method is MSE=0.0082 and MAE=0.003 less than that of the existing methods.