Predicting Weather Conditions for Improving Crop Productivity Using Machine Learning Approaches

Abstract

Agricultural systems are becoming increasingly prone to a range of non-climatic and climatic stressors. Constituently, there is food insecurity and economic distress throughout the world. To address these challenges, machine learning (ML) techniques have gained attention in the field of agriculture. Monitoring weather information is crucial for resource management and prioritizing the areas where efforts could be made to strengthen agricultural production. The objective of this chapter is to explore the effectiveness of ML for future simulation of agro-climatological variables. The chapter investigates the methodologies, limitations, and potentialities of ML related with employing ML for weather prediction in the context of sustainable agriculture. Chapter it is stressed on the potential benefits of these predictive models for enhancing crop management methods, resource allocation, and overall agricultural productivity. The use of ML in weather forecasting offers the prospect of helping sustainable and resilient agricultural practices, ultimately contributing to global food security.