Khatun, S.; Halder, M.; Arafat, M.Y.; Sheikh, M.N.A.; Adnan, D.M.N.; Kabir, S.S. An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification. Preprints2023, 2023090702. https://doi.org/10.20944/preprints202309.0702.v1
APA Style
Khatun, S., Halder, M., Arafat, M.Y., Sheikh, M.N.A., Adnan, D.M.N., & Kabir, S.S. (2023). An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification. Preprints. https://doi.org/10.20944/preprints202309.0702.v1
Chicago/Turabian Style
Khatun, S., Dr. Md. Nasim Adnan and Sk. Shalauddin Kabir. 2023 "An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification" Preprints. https://doi.org/10.20944/preprints202309.0702.v1
Abstract
Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.
Keywords
Potato Leaf Diseases Detection; Classification; Transfer learning; Convolution Neural Network; Deep learning; Pre-trained Models
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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