Research Article
A Study on the Performance of Deep Learning Models for Leaf Disease Detection
@ARTICLE{10.4108/eetiot.4592, author={G Sucharitha and M Sirisha and K Pravalika and K. Navya Gayathri}, title={A Study on the Performance of Deep Learning Models for Leaf Disease Detection}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2023}, month={12}, keywords={InceptionV3, MobileNet, DenseNet121, Inception-ResNetV2, leaf Disease, Pretrained models, ResNet152V2, Classification}, doi={10.4108/eetiot.4592} }- G Sucharitha
M Sirisha
K Pravalika
K. Navya Gayathri
Year: 2023
A Study on the Performance of Deep Learning Models for Leaf Disease Detection
IOT
EAI
DOI: 10.4108/eetiot.4592
Abstract
The backbone of our Indian economy is agriculture. Plant diseases are a key contributor to substantial reductions in crop quality and quantity. Finding leaf diseases is a crucial job in the study of plant pathology. So, Deep learning models are essential for classification objectives with positive outcomes. Many different methods have been employed in recent years to classify plant diseases. This work has aided in identifying and categorizing a plant leaf disease. Images of Tomato, Potato, and Pepper plant leaves from the PlantVillage Database, which includes fifteen disease classifications, were used in this study. The pre-trained Deep learning models like InceptionV3, MobileNet, DenseNet121, Inception-ResNetV2, and ResNet152V2 are utilized to diagnose leaf diseases. The classification of both healthy and various sorts of leaf illnesses is taught to deep learning models.
Copyright © 2023 G. Sucharitha et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
