Development of an IoT Based Irrigation Control System using Convolutional Neural Network

Abstract

The automation of irrigation activities has the potential to revolutionize traditional manual and static irrigation practices, leading to increased productivity with reduced human intervention. Manual irrigation practices often result in water wastage or inadequate water supply to specific crops, as different crops have varying water requirements (crop water need). Moreover, manual irrigation methods consume significant time and effort, especially when the farmland is located at a distance. This paper presents an IoT-based irrigation system that utilizes computer vision technology to capture and recognize crops in the irrigation field using a Convolutional Neural Network (CNN) model. The developed system continuously monitors and maintains the optimal soil moisture content for each specific crop, employing soil moisture and temperature sensors. The control unit of the system is implemented using the Raspberry Pi 3b+ platform. The performance of the developed system was evaluated using two key metrics: Accuracy and Response time. The CNN model achieved high accuracy, with a stabilized accuracy of 95 percent after 50 epochs of training and validation, using a dataset of 800 pictures. This indicates the system's capability to accurately identify crops in the field. The response time of the system was assessed through ten trials, resulting in an average response time of 14.3 seconds, which is considered satisfactory. The findings of this study demonstrate the effectiveness of the proposed IoT-based irrigation system in automating irrigation processes and optimizing water usage. By integrating crop recognition, soil moisture monitoring, and temperature sensing, the system ensures efficient irrigation practices, reducing water wastage and minimizing human effort. The successful implementation of the developed system paves the way for intelligent and dynamic irrigation systems, fostering higher agricultural productivity and sustainable water resource management.