Bioagrinet: Honey Badger-Optimized Saint-Bilstm Model for Boosting Crop Yield and Nutrient Quality

Background: Agriculture is challenged to satisfy rising food needs in the face of an expanding world population, in addition to environmental and genetic limitations that influence crop yield and nutritional quality. Machine Learning (ML) and Deep Learning (DL) are solving these problems as they enable the accurate establishment of predictions based on large datasets in the field of agriculture. The current models do not consider complex interactions between the treatment, genetic, and environmental variables. Also present of models are prone to fail in generalizing in diverse agricultural environments and cannot be interpreted by the end-users such as farmers. Proposed Method: The study provides a BioAgriNet model, a Honey Badger-Optimized SAINT-BiLSTM that integrates the genotype, environment, and treatments to predict crop yield and nutrient quality. BioAgriNet is an opportunity that exploits the hybrid architecture in the form of Self-Attention and Inters ample Attention Transformer (SAINT) combined with BiLSTM with Honey Badger Optimization (HBO) to select the features, and thus is a stable, interpretable, and efficient model that will be applied in agricultural prediction. Unlike the traditional methods, it not only provides the prediction of the yield and quality of the nutrients but also it provides the contribution to the environmental and genetic factors. Findings: The R 2 of the model was 0.985 which explained 98.5 percent of the variance in yield. RMSE was 0.005, and MAE was 0.03, showing high accuracy in prediction. MAPE was 2.5%, reflecting minimal percentage error. BioAgriNet performed better than comparative baseline models like CNN-LSTM, which had lower R 2 values and higher error rates, reflecting its better predictive ability. Edge Over This Domain: The new model enhances the accuracy of predictions, explainability, and real-time usage, and is an important asset in sustainable agriculture, improving food security, and maximizing market value.