Publications

Abstract : Precision agriculture refers to the application of modern tools and techniques to increase crop productivity in an environment-friendly manner. In the proposed work, a model of self-adaptive system for precision agriculture is developed. This Internet of Things (IoT)-based agriculture system mainly incorporates two functions, automated irrigation and pest detection and is augmented with machine learning models to make it self-adaptive. It handles the sensor failure events automatically by predicting the possible sensor values and keeps the system running without interruption. The system notifies the user about the failure so that it can be replaced later, thus avoiding abrupt termination or malfunctioning of the system. Another adaptive aspect of the proposed system is that it can adjust the system parameters based on prediction of stochastic environmental parameters like rain and temperature. Occurrence of rain is predicted by a machine learning model, and based on this, the system parameters like frequency of getting moisture sensor values are adjusted. This adaptation is fruitful during occurrence of continuous rain when the soil is wet and the moisture content information can be collected less frequently, thus saving the power consumption involved in data collection. The learning models long short-term memory (LSTM) and random forest are used in implementing adaptive functions. The automated irrigation becomes active on fixed times, and the amount of water dispensed is based on the values obtained from soil moisture sensors deployed. The pest detection module captures the images of field and detects mainly the bird pests attacking the crop. The object detection technique, Yolo4, is used to spot the pest.