Seeding Sustainability: Machine Learning Applications In Agricultural Climate Change Adaptation Strategies

Abstract:
As global agriculture faces escalating challenges from climate change, the need for innovative and adaptive solutions becomes imperative. This research explores the integration of machine learning techniques in agricultural practices to enhance climate change adaptation strategies. The study focuses on precision agriculture, leveraging data-driven insights to optimize resource utilization, mitigate environmental risks, and bolster resilience against climatic uncertainties. Our findings underscore the efficacy of machine learning models in predicting and managing crop responses to changing climatic conditions, facilitating informed decision-making for farmers. The proposed framework combines advanced analytics, sensor technologies, and automated systems to foster sustainable agricultural practices in the era of climate change.