IOT-Based Pest Classification And Automatic Irrigation For Precision Agriculture Using Wireless Sensor Networks

Abstract

Automatic pest classification is a very important step to protect the agricultural crops against the attack of pest. This helps to increase the crop yield by reducing the pests that affect agricultural productivity. In addition, automatic irrigation systems aid in the enhancement of agricultural land productivity by computing optimal amount of water required by the plants. In this research, we proposed a new technique called IoT-based pest classification and automatic irrigation algorithm (IPCAI) using wireless sensor networks. In this technique, sensors like moisture sensor, temperature sensor and camera sensors are integrated to Arduino Microcontroller module. The data acquired by these sensors are processed using Raspberry Pi module that is connected to the cloud. The proposed IPCAI machine learning algorithm is embedded into the Raspberry Pi module that classifies the type of pest and also computes the optimal amount of water required by the crops. Based on the type of pest being detected, suitable pesticide is sprayed to the crops to improve the crop yield. This helps in the prevention of spreading of pests. It was found that the proposed algorithm classifies 40 different type of pest with very high accuracy. In addition, the proposed automatic irrigation system helps to conserve enormous quantities of water. The IoT-module connected to the Raspberry Pi helps to upload the data collected by the sensors, pest classification result, and water requirement result to the cloud. From the cloud, the data is transmitted to the farmer’s mobile, using which the famers can continuously monitor the crop land from remote locations. The proposed pest classification algorithm achieved high specificity of 95.86% with a precision rate of 96.69%.