Advancing Agriculture 6.0: A Novel Residual Recurrent Unit for Crop Recommendation Using IoT Data

In the era of smart technologies, Agriculture 6.0 utilized the most advanced technologies like artificial intelligence to enhance smart farming systems. It processed an optimal crop selection to maximize agricultural productivity based on specific environmental and soil conditions. In this work, the new Deep Learning (DL) method is presented such as the Residual Recurrent Unit (RRU) Model which has two key modules Adaptive Scaling in the Transform Module and a Residual Connection Module. These modules optimized the model's sensitivity to varying inputs and also allowed untransformed data to directly influence the cell state to improve gradient flow. These features enhance the model performance by similar past and present inputs effectively. To further optimize performance, Siberian Tiger Optimization (STO) was used to tune the hyperparameter and attain the best possible result. The experimental result of the novel RRU model validated for both with and without STO based on various metrics like Accuracy, Precision, Recall, F1-Score, ROC, Loss, Cohen's Kappa and Matthews Correlation Coefficient etc. based on the validation, the proposed RRU model has achieved a superior performance in all aspects than traditional methods. Also, this work included a hardware setup and collected data from a real field to investigate the model’s performances based on soil and environmental factors. Therefore, real data results based on the novel RRU model provided an accurate crop recommendation and it is majorly supportive of precision agriculture with effectiveness in future.