Predictive Analytics in Plant Biotechnology: Using Data Science to Drive Crop Resilience and Productivity

Abstract

Data science and predictive analytics are revolutionizing plant biotechnology by revealing crop performance and tolerances. Data science is important in a global context where agricultural demand is rising and crops' yields, resilience, and sustainable resource usage are maximized daily. We explore predictive models in plant biotechnology and how they may be developed utilizing agronomic, environmental, phenotypic, and genomic data to improve agricultural solutions. Predictive analytics extrapolates genome, transcriptomics, and proteomics data to promote precision farming and climate-resilient crop adaptive breeding. Agricultural data science uses IoT sensors, drones, and image technologies, but integration and data quality are still difficulties. The review also explores machine learning approaches including decision trees, neural networks, regression, and others to help predictive analytics overcome restrictions. These models can quantify resilience and response to biotic and abiotic stresses, predict yields, and choose breeding genes. Examples demonstrate how predictive models can boost crop resilience, yields, and water and pest management early intervention. Predictive analytics in plant biotechnology faces data shortages, processing needs, and model interpretability challenges. These barriers may prohibit many agricultural stakeholders from adopting advanced models like deep neural networks. The study concludes that plant scientists, data scientists, and agronomists must work together, integrate AI with multi-omics for advanced predictive modeling, and use blockchain for data security. These advances can help predictive analytics improve sustainable agriculture by fostering resilient crop growth and resource efficiency for a more predictable food supply.