Applications Of Machine Learning in Predicting Crop Yields for Sustainable Agriculture

Abstract

Modern agriculture is increasingly adopting data-driven techniques to enhance productivity and sustainability. This comprehensive framework begins with Data Collection and Preprocessing, involving the meticulous sourcing of data from various channels and the critical processes of ensuring Data Quality and Cleaning. Machine Learning Models, such as Regression Models (including Linear Regression, Random Forest Regression, and Support Vector Machines), Time Series Analysis, and Deep Learning Models, play a pivotal role in predicting crop yields. These models are valuable tools that empower farmers and stakeholders to make informed decisions, optimize resource allocation, and respond to the ever-evolving challenges in agriculture. In this context, Predictive Features are harnessed, including Weather Data, Soil Quality and Composition, Pest and Disease Data, and Remote Sensing and Satellite Imagery. These features provide a holistic understanding of the factors that influence crop yields and enable the adoption of sustainable practices. However, the process is not without its Challenges and Considerations, encompassing data quality, model selection, local variability, interpretability, and adaptation to climate change. The Benefits of Yield Prediction in Sustainable Agriculture are extensive and include optimized resource management, early pest and disease control, sustainable land use, climate resilience, and data-driven decision-making. This data-driven approach supports the critical mission of ensuring food security, conserving resources, and building resilient agricultural systems for the future. Yield prediction is a transformative approach that not only increases agricultural productivity but also fosters sustainability and resilience in agriculture