Developing an efficient irrigation scheduling system using hybrid machine learning algorithm to enhance the sugarcane crop productivity

As a crop with maximum utilization of water, Sugarcane is responsive to water management system throughout lifetime of the crop. Since, due to the fluctuation of climatic variables, the demand of water resources becomes dynamic. This work focuses on the different irrigation system for sugarcane yield using AquaCrop and neural network models. AquaCrop model processes the predetermined data of weather from 1988 – 1989 to 2019 – 2020, soil data, crop data and irrigation system to acquire certain amount of sugarcane yield. The changes occurred in the weather variables consequently affect the crop parameters and irrigation system; thus usage of water is difficult to schedule. So, soil permanent wilting point sensor is deployed in this model to extract the amount of water presents in the soil. The moisture level of soil data is further stored in the cloud called Thingspeak. The soil data are passed on to the cloud via WiFi modem. The future weather data from 2020-2021 to 2049-2050 which are simulated by CCSM4 model and soil sensor data are integrated; then implemented using both conventional neural network and optimized deep learning models in order to classify the irrigation strategies. The firefly optimization helps to optimize the scheduling of the irrigation according to weather and soil data. Hence, water utilized by optimized deep learning model is reduced and predicted more accurately as compared with standard AquaCrop and conventional neural network models. The result of this research is capable of providing an optimized irrigation system with efficient consumption of water saving measures.