Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

Abstract

The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes. Further in this paper, the adoption of the mixed-method approach Deep Convolutional Neural Network (Deep CNN) has assisted the research in increasing the effectiveness of the proposed method. Deep CNN is used for image recognition and is a class of deep-learning neural networks. CNN is popular and mostly used in the field of image recognition. A dataset of images with three main leaf diseases is selected for training and testing the proposed model. After the image acquisition and preprocessing process, the DeepCNNmodel was trained to detect and classify three rice diseases (Brown spot, bacterial blight, and blast disease). The proposed model achieved 98.3% accuracy in comparison with similar state-of-the-art techniques.