Analysis of An Intellectual Mechanism of a Novel Crop Recommendation System Using Improved Heuristic Algorithm-Based Attention and Cascaded Deep Learning Network

Abstract

This article introduces an innovative crop recommendation system that leverages an attention-based cascaded deep learning network (AACNet) optimized by an improved migration algorithm (IMA). The system is designed to address the inefficiencies of traditional crop recommendation methods by providing precise, real-time suggestions tailored to specific agricultural factors such as weather, soil type, and time. The AACNet employs recurrent neural networks (RNN) and gated recurrent units (GRU) to analyze time-sensitive agricultural factors, such as weather patterns and soil conditions, while the attention mechanism prioritizes the most significant features for accurate crop recommendations. The IMA optimizes the deep learning network, enhancing the system’s accuracy, precision, recall, and execution time. Experimental results demonstrate that the proposed system outperforms traditional methods, marking a significant advancement in precision agriculture. The system’s potential to revolutionize farming decision-making processes by optimizing resource allocation, reducing costs, and increasing crop yields underscores its importance in global agricultural challenges. This research represents a transformative step towards informed, efficient, and sustainable farming practices.