Abstract
Soil salinity is a serious land degradation issue in agriculture. It is a major threat to agriculture productivity. Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction (LF) of irrigation water. For the leaching process to be effective, the LF of irrigation water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration (ET) rate. The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning (ML) based decisions are required to determine the calibrated Evapotranspiration (ETc) rate. ML-assisted ETc is proposed to adjust the LF according to the ETc and soil salinity level. A regression model is proposed to determine the ETc rate according to the prevailing temperature, humidity, and sunshine, which would be used to determine the smart LF according to the ETc and soil salinity level. The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration (ETo) determination. The validation of the model from the test dataset reveals the accuracy of the ML model in terms of Root mean squared errors (RMSE) are 0.41, Mean absolute errors (MAE) are 0.34, and Mean squared errors (MSE) are 0.28 mm day-1. The applications of the proposed solution in a real-time environment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.