Harris Hawks Optimization and Attention Networks to Explainable AI Crop Recommendation

Crop recommendation systems are crucial in contemporary agriculture in predicting the best crops depending on the nutrients of soil and past yield. Nevertheless, conventional agriculture is based on experience and trial and error approach which tends to lead to sub-optimum production. The analysis of the multivariate soil data, the environmental conditions, and the yield records is quite challenging to analyze manually. Moreover, the traditional machine learning models are not interpretable, which will restrict the trust and adoption of farmers. This study surpasses these limitations with an unified framework based on Harris Hawks Optimization (HHO), Attention-Residual Networks and explainable AI. HHO is effective to find the most important soil and crop characteristics whereas Attention-Residual Networks involving complex associations among soil nutrients and historical yields serve to make precise predictions. SHAP (SHapley Additive explanations) offers the benefit of transparency that explains the effects of individual features on crop recommendations and leads to trust and understanding between farmers and agronomists. This system, which is implemented in Python, facilitates decision-making accuracy, sustainable farming practices as well as facilitates a practical usability since it generates clear and interpretable insights that enable agricultural stakeholders to make informed decisions when it comes to crop selection.