Editorial
Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models
@ARTICLE{10.4108/eetiot.4815, author={Surendra Reddy Vinta and Ashok Kumar Koshariya and Sampath Kumar S and Aditya and Annantharao Gottimukkala}, title={Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={1}, keywords={Leaf illness, Image processing, crop disease, Deep learning}, doi={10.4108/eetiot.4815} }- Surendra Reddy Vinta
Ashok Kumar Koshariya
Sampath Kumar S
Aditya
Annantharao Gottimukkala
Year: 2024
Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models
IOT
EAI
DOI: 10.4108/eetiot.4815
Abstract
Despite rapid population growth, agriculture feeds everyone. To feed the people, agriculture must detect plant illnesses early. Predicting crop diseases early is unfortunate. The publication educates farmers about cutting-edge plant leaf disease-reduction strategies. Since tomato is a readily accessible vegetable, machine learning and image processing with an accurate algorithm are used to identify tomato leaf illnesses. This study examines disordered tomato leaf samples. Based on early signs, farmers may quickly identify tomato leaf problem samples. Histogram Equalization improves tomato leaf samples after re sizing them to 256 × 256 pixels. K-means clustering divides data space into Voronoi cells. Contour tracing extracts leaf sample boundaries. Discrete Wavelet Transform, Principal Component Analysis, and Grey Level Co-occurrence Matrix retrieve leaf sample information.
Copyright © 2024 S. R. Vinta et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 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.
