Decision Support System for On Farm Crop Water Irrigation Scheduling using Machine Learning approaches

Abstract

In India, irrigation is the largest consumer of fresh water and is drawing about 90% of groundwater. The requirement of irrigation system is much needed for the region such as Central Punjab, which occupies nearly 97.95% of the gross irrigated area for agricultural production. The water consumption is very high in commonly cultivated crops in Punjab that are Wheat, Maize, and Rice. This requires modern technologies in water management to meet the agricultural challenges. Hence, this system referred as Irrigation Water Management (IWM), is poised to be a key driver of smart farming to meet crop water requirement with a sufficient economic return without any damage to land and soil. The major challenge in agriculture sustainability and dawdling is to utilize every drop of fresh water effectively and efficiently. The studies on water shortage suggest the development of innovative irrigation methods such as controlled deficit irrigation, partial root drying, and continuous deficit irrigation. In this context, the controlled irrigation, climate, soil fertility, crop quality, and time management are essential to the Decision Support System (DSS) to maximize the crop yield with minimum consumption of water. Advanced Analytics and DSS can help farm managers in taking decision to solve complex irrigation problem. The Reference Evapotranspiration (ETo) is one of the most valuable parameters for hydrological, climatologist investigation, and water resources management. An exact estimate of ETo is necessary to analyze the water demand of irrigated agriculture, crop-water balance and improve the water quality. However, ETo estimation is very difficult to achieve due to its dependency on many input parameters. Therefore, the primary objective of the research is to establish regression models for the estimation of ETo with limited climate parameters. The estimation of daily ETo can help in real-time prediction of crop evapotranspiration and crop irrigation demand.