Precision Agriculture Meets AI: Predicting Nutritional Crop Outcomes from Genomic Data

Abstract

Global malnutrition can now be addressed and crop nutritional quality improved thanks to developments in precision agriculture, genomics, and artificial intelligence (AI). In order to predict and optimize nutrient-enriched crop traits, this study suggests an integrated AI-driven framework that combines data from precision agriculture, genomics, and malnutrition epidemiology. In order to produce useful insights for crop improvement, this framework analyzes multi-dimensional data that includes genetic markers, environmental factors, and dietary deficiency patterns using a combination of machine learning models, such as Logistic Regression, Naive Bayes, and Neural Networks.

With the highest accuracy (97.38%) and coefficient of determination (R2 = 0.9584), the lowest prediction errors (MSE: 0.0187; RMSE: 0.1367), and consistent cross-validation stability (mean CV accuracy: 97.35%, std. dev: 0.0042), a thorough comparative analysis reveals that Logistic Regression is the best predictive model. Despite having slightly lower accuracy (95.64%) and higher error rates, Naive Bayes has the benefit of almost instantaneous training times, which makes it appropriate for situations requiring quick deployment. Even though neural networks can represent intricate relationships, their accuracy was relatively low (92.11%) and their errors were higher, indicating that more data augmentation and hyperparameter optimization are required.

SHapley Additive exPlanations (SHAP), which identifies important genomic and environmental features influencing nutrient trait predictions, improves the interpretability of model predictions. Data-driven breeding and precision agriculture decisions are supported by this transparency, which fosters understanding and trust between geneticists and agronomists. Nevertheless, there are still difficulties, such as the intricacies of integrating diverse datasets, problems with scalability, the high expense of genomic technologies, and their restricted suitability for smallholder farming environments.

This study highlights how crop biofortification strategies that are in line with public health and sustainable agricultural development objectives can be enhanced through AI-driven multi-domain data integration. Expanding dataset diversity, enhancing model generalizability across crops and regions, and promoting interdisciplinary collaborations will be the main goals of future efforts to hasten the adoption of precision agriculture technologies for the improvement of global nutrition.