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Plant leaf disease detection and classification using image processing techniques
, Shreekant Ghorpade, Mayur Adawadkar
Published in IEEE
Volume: 2.0
Issue: 4.0
Pages: 139.0 - 144.0
Agricultural products are the primary need for every country. If plants are infected by diseases, this impacts the country's agricultural production and its economic resources. This paper presents a system that is used to classify and detect plant leaf diseases using deep learning techniques. The used images were obtained from (Plant Village dataset) website. In our work, we have taken specific types of plants; include tomatoes, pepper, and potatoes, as they are the most common types of plants in the world and in Iraq in particular. This Data Set contains 20636 images of plants and their diseases. In our proposed system, we used the convolutional neural network (CNN), through which plant leaf diseases are classified, 15 classes were classified, including 12 classes for diseases of different plants that were detected, such as bacteria, fungi, etc., and 3 classes for healthy leaves. As a result, we obtained excellent accuracy in training and testing, we have got an accuracy of (98.29%) for training, and (98.029%) for testing for all data set that were used.
About the journal
JournalData powered by TypesetInternational Journal of Innovative and Emerging Research in Engineering
PublisherData powered by TypesetIEEE
Open Access0