Machine Learning for Sustainable Agriculture: Enhancing Crop Yield Predictions and Resource Management

Machine Learning for Sustainable Agriculture: Enhancing Crop Yield Predictions and Resource Management

Authors- Lakshmi. N

Abstract--Sustainable agriculture is essential to ensure the efficient use of resources and meet the growing global demand for food. With the increasing challenges posed by climate change, population growth, and environmental degradation, there is an urgent need for innovative solutions that improve crop yield and resource management. Machine learning (ML), a subset of artificial intelligence (AI), offers significant potential to transform agriculture by providing data-driven insights into crop performance, soil health, weather patterns, and resource allocation. This paper examines the role of machine learning in sustainable agriculture, with a focus on its applications in crop yield prediction, pest and disease management, soil quality monitoring, and water usage optimization. Additionally, it explores the benefits of ML in enhancing precision agriculture and reducing the environmental impact of farming practices. Despite its potential, the adoption of machine learning in agriculture faces challenges related to data quality, infrastructure, and farmer education. The paper concludes with a discussion of the future of ML in agriculture, highlighting the need for continued research and collaboration between technology providers, farmers, and policymakers.

DOI: /10.61463/ijset.vol.13.issue2.371