Leaf Disease Identification using ResNet

• Published in: 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) pp. 1 - 5
• Main Authors: Sirenjeevi, P., Karthick, J. Madhu, Agalya, K., Srikanth, R., Elangovan, T., Nareshkumar, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.01.2023
• Subjects: CNN; convolutional neural network; Deep learning; Hardware; Image segmentation; Knowledge discovery; leaf disease; ResNet; Stars; Support vector machines; Transforms
• ISBN: 9798350334357
• DOI: 10.1109/ICECONF57129.2023.10083963

The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and widely used. In order to explore the detection and classification of apple leaf illnesses, we made use of data sets containing examples of apple grey-spot disease, black star disease, cedar rust disease, and healthy leaves. SVM classifier for image segmentation, ResNet and VGG convolutional neural network models were utilized for comparison and improvement respectively. In our prosed method ResNet-18, which had less layers of the ResNet network, achieved greater recognition effects by obtaining an accuracy rate of 98.5 percent.