Machine Learning for Sustainable Agriculture: Enhancing Crop Yield Predictions and Resource Management
Authors:-Ashok.P
Abstract-The global population is expected to surpass 9 billion by 2050, placing unprecedented demand on agricultural systems to produce more food while minimizing environmental impact. Sustainable agriculture, which focuses on producing food while preserving environmental health, is vital for ensuring future food security. Machine learning (ML), a powerful subset of artificial intelligence (AI), holds significant potential for enhancing agricultural practices by improving crop yield predictions, optimizing resource management, and enabling precision farming techniques. This paper explores how ML algorithms are being applied to sustainable agriculture, from predictive analytics for crop yield forecasting to real-time monitoring of soil conditions and pest management. It examines key ML techniques such as supervised learning, unsupervised learning, and reinforcement learning and their role in enhancing agricultural sustainability. Furthermore, the paper highlights the challenges and ethical considerations involved in implementing ML in agriculture and discusses the future outlook for AI-driven innovations in the sector.
