Author(s):
In precision agriculture, the ability to accurately predict crop phenotypes is crucial for optimizing yields, improving resource efficiency, and ensuring food security. This study explores an integrated approach to crop phenotype prediction, combining genomic, environmental, and phenotypic data. By leveraging advances in genomics, high-throughput phenotyping, and environmental monitoring, we develop a predictive model that enhances the accuracy of crop trait forecasting. The integration of multi-source data, including genomic markers, climate variables, soil characteristics, and real-time phenotypic observations, enables more precise and dynamic predictions of crop performance under varying conditions. We demonstrate how this integrative framework can inform breeding strategies, crop management decisions, and climate resilience efforts, ultimately advancing the goals of sustainable and precision agriculture. The study highlights the potential of systems biology and data-driven techniques in shaping the future of crop production
