Improving Water-Efficient Irrigation in Terrain Durio Zibethinus Farming Using Hybrid Ant Colony Optimization-Based Soil Moisture Prediction Model

Abstract

The vegetation stage of Durio Zibethinus trees is characterized by active root development, leaf expansion, and the initiation of reproductive structures. During this crucial phase, adequate irrigation is necessary to satisfy the trees' water requirements. A well-irrigated durian plantation encourages effective nutrient absorption, resulting in healthier trees with increased pest and disease resistance. Understanding the water needs of durian trees is essential for irrigation management to optimize water application and prevent water stress and waterlogging. Typically, sensors measure the soil moisture within the root zone. However, installing soil moisture sensors at each tree is laborious and prohibitively expensive. Using climatic data to forecast the value is a viable option in such a scenario. Climate data are used to create soil moisture predictions incorporated into the irrigation model. This research employs Ant Colony Optimization- Support Vector Regression (ACO-SVR) to predict soil moisture levels. The model is compared to other optimization methods, and its accuracy is assessed using statistical methods. Finally, the prediction models' findings determine the irrigation volume and schedule.