Machine Learning, Deep Learning Models for Agro-Meteorology Applications

Increasing water, energy, and food demand from the rising population of India needs organized water management using clean and sustainable energy with improved crop yield. Agricultural Meteorology is the application of meteorology to the measurement and analysis of the physical environment in the agricultural field. It is the performance of the weather and climate factors that has direct relevance to agriculture and their outcome in crop production. Over the present period, there have been massive improvements in weather prediction systems; yet, their direct applications have not been completely explored in effective crop water management. The aim of this paper is to review the Machine Learning (ML) and Deep Learning (DL) models for agro-meteorological applications. ML and DL-based Climate-smart agriculture approaches are discussed in this paper to maximize crop yield and minimize water use. This review study can be used as an effective platform for irrigation planning, design, and management in smart farming and to address the current challenges in agro-meteorological applications.