Root Depth Prediction Using Machine Learning for Effective Root Zone Injection Irrigation through IoT Automation - Vivekanandhan, V.; M., Christopher; M, Dilipkumar; G., Gopal (Research Parks Publishing LLC, 2022)
Agriculture plays an important role in producing food supply for survival. The agriculture practice is performed by multiple factors such as soil type, fertilisers, plant samplings, irrigation practice, etc. On these factors on which agriculture depends, irrigation is one of the major factors for the better yield of crops, just like any other factor. Therefore, efficient irrigation practice has to be performed to increase cultivation production and preserve available water resources for optimal usage. Traditional methods of irrigation practice are well suited for situations with surplus water resources but not very efficient when it comes to scarce places. So, we propose a new IoT-driven root zone injection method of irrigation which is estimated to perform required irrigation practices in places of high water scarcity. This method is performed with the help of machine learning, IoT devices, wireless neural networking of sensors, and root zone injection equipment for automation. The mechanism starts with collecting real-time data from the agricultural field for specified crop types by using a wireless neural network of sensors and forming the dataset. Once the dataset is formed, it will be processed and cleaned to feed into machine learning algorithms. The machine learning algorithm (here, it is linear regression) will make the required prediction for the water content needed for the irrigation process for that particular day. The dynamic estimation is made as the water content required will vary from the growing phases of plants where it is minimum at the initial phase, peak at middle and reduce or increase depending on the plant species at later phase of growth. This estimated water content is then delivered to the plants through the irrigation process, governed by the IoT devices, which have the procedures encoded for irrigation. ML prediction guides the IoT system on how much water to deliver to the plants. Finally, the injection setup of the root zone passes the water directly to the underground root zones. Thus, completely preventing evaporation wastage and accurate water content estimation and supply, achieving optimal irrigation practice.