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

Computer Systems Science & Engineering 2023年第11期47卷 1385-1413页

作者： Fawad Ali Shah Habib Akbar Abid Ali Parveen Amna Maha Aljohani eman a.aldhahri Harun Jamil Department of IT The University of HaripurHaripur22620Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47080Pakistan Department of Computer Science GANK(S)DC KTS HaripurHaripur22620Pakistan College of Pharmacy Gachon University Medical CampusNo.191HambakmoeroYeonsu-guIncheon21936Korea Software Engineering Department College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia College of Computer Sciences and Engineering Department of Computer Science and Artificial IntelligenceUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering Jeju National UniversityJejusi63243Korea

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 *** 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 *** 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 *** 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 *** CNN is used for image recognition and is a class of deep-learning neural *** 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 *** the image acquisition and preprocessing process,the Deep CNN model 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.

关键词： Rice plant disease detection convolution neural network image classification biological classification

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