Climate-Smart Agriculture: AI-Based Solutions for Enhancing Crop Resilience and Reducing Environmental Impact

Abstract

Climate change poses significant challenges to global food security, necessitating the use of AI-based climate-smart agriculture (CSA) technologies to improve crop resilience, reduce environmental impact, and optimize resource use. AI-based interventions can reduce carbon emissions by 30–50% and boost agricultural productivity by up to 25%. Machine learning approaches can forecast crop yields with 90% accuracy, facilitating climate adaptation. AI insect surveillance can reduce pesticide application by 30%, and artificial irrigation systems can save up to 40% water. IoT sensors and remote sensing improve soil health monitoring and carbon sequestration practices, increasing soil organic carbon stocks by 20–35%. AI-powered predictive analytics can provide early alerts for storms, reducing agricultural losses by 15–20%. Automation and robotics can reduce post-harvest losses by up to 35%. Blockchain and AI can ensure transparency in sustainable agricultural supply chains and carbon credit markets. This blending of AI and CSA can significantly reduce climate change implications. The use of AI in smallholder agriculture faces challenges such as inflated implementation costs, reduced digital literacy, and concerns around data privacy. Fixing these issues requires economical solutions, agricultural training initiatives, localized artificial intelligence models, and legislative changes.