Plant Disease Detection using Convolutional Neural Network

Abstract

The global rise in population has led to a shortage of raw materials and food supplies. The agricultural sector has become the primary and most vital source to overcome this particular constraint. However, the industry itself is facing the challenge of pests and various crop diseases. Battling this has been the significant focus of the sector for decades. Still, due to the technology gap that existed earlier, there existed a constraint on identifying the diseased crops on a massive scale. Nevertheless, today, with the improvement of technologies such as drones, IoT devices, and higher processing speeds combined with data analysis and machine learning, the problem of identification can be resolved quickly. This paper aims to provide a brief description of existing solutions that have been published and focuses on the more efficient machine learning model based on conventional neural networks (CNN) that we have developed. This machine learning model can be deployed on IoT devices, mobile phones, and drones and cameras that farmers can utilize to identify the diseased crops on a massive scale and take the necessary precautions not to let the disease spread and affect the supply produced. The proposed model using CNN was trained using images from plant village dataset and attained an accuracy of 94.87% in identifying the diseased plants with the help of image processing by OpenCV. Finally, the paper showcases the detailed analysis of the proposed scheme and results obtained by the model.