Machine Learning Approaches to Enhance Water Conservation in Agriculture

Abstract

In recent years, the urgency to optimize water usage in agriculture has grown due to changing climatic conditions and increasing water scarcity. As agriculture consumes a significant portion of freshwater globally, there's a paramount need for efficient water management techniques. This research classifies the emerging machine learning (ML) techniques for water conservation in agriculture into four main categories, elucidating the progressive journey from data procurement to farmer implementation. The first category, Data Collection and Monitoring, underscores the importance of precise and continuous data acquisition. Here, advanced techniques like deploying in-situ soil moisture sensors, leveraging satellite data for crop health and moisture monitoring, and integrating weather forecasts have been explored to proactively anticipate irrigation demands. Secondly, the Analysis and Prediction phase utilizes the collected data, employing predictive analytics to project weather patterns, soil moisture levels, and crop water requirements. Additionally, the segment investigates the use of classification models for early identification of potential crop diseases and water stress zones. It also delves into algorithm-driven approaches to determine optimal planting patterns, ensuring maximum yield with minimal water usage. In the Irrigation Management category, the focus shifts to actionable insights. Reinforcement learning models are designed to discern and implement optimal irrigation strategies. Concurrently, the research explores the potential of data analytics in drip irrigation optimization and the role of anomaly detection models in identifying irregularities in soil moisture, a crucial measure to prevent wastage. Lastly, the User Interface and Recommendations section emphasizes bridging the gap between sophisticated ML models and on-ground agricultural practitioners. By establishing user-friendly dashboards, farmers receive tailored, real-time data-driven recommendations. The integration of diverse data sources ensures a holistic analysis, with deep learning models further enhancing accuracy and predictive capabilities.