State-of-Art Deep Learning Based Tomato Leaf Disease Detection

DOI

10.2991/ahis.k.210913.038How to use a DOI?

Keywords

Faster RNN, Random Forest Tree, SVM, Tomato Leaf disease detection, YOLO

Abstract

In India, the tomato plant is a popular staple food with high commercial value and considerable production capacity; however, the quality and quantity of the tomato harvest decreases due to a variety of diseases and henceforth leads to great financial loss for farmers. With lack of agricultural professions to assist the farmers, a deep learning (DL) based user friendly, just-in-time mobile is proposed for the detection of crop diseases for assisting farmers to know about the type of tomato disease and the remedy for the same. Two DL based methods: YOLO and Faster RNN have been used for detection; followed by classification using SVM and Random forest tree. YOLO and Random forest tree resulted in accuracy in the range of 90% to 95%. The developed app provides option to the farmer to operate in English as well as in local language Kannada of Karnataka state of India.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Asha Gowda Karegowda
AU  - Raksha Jain
AU  - G Devika
PY  - 2021
DA  - 2021/09/13
TI  - State-of-Art Deep Learning Based Tomato Leaf Disease Detection
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 303
EP  - 311
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.038
DO  - 10.2991/ahis.k.210913.038
ID  - Karegowda2021
ER  -