Application of machine learning techniques in rice leaf disease detection. (2022)
- Record Type:
- Journal Article
- Title:
- Application of machine learning techniques in rice leaf disease detection. (2022)
- Main Title:
- Application of machine learning techniques in rice leaf disease detection
- Authors:
- Pallathadka, Harikumar
Ravipati, Pavankumar
Sekhar Sajja, Guna
Phasinam, Khongdet
Kassanuk, Thanwamas
Sanchez, Domenic T.
Prabhu, P. - Abstract:
- Abstract: The automated leaf disease diagnosis system is a precision agriculture system that predicts sickness by analyzing images of infected leaf disease with Computer Vision, Image Processing, and Machine Learning algorithms. Thanks to automated disease detection technology, which speeds up the diagnosis procedure, the farmer can make an informed decision about a plant sickness. Previously, the farmer had to submit the infected leaf to a pathology lab, where the pathologist confirmed the disease, a time-consuming procedure. As a result of the delayed reaction, crop productivity declines. As a result, it is important to automate the disease detection system in order to increase crop yield. This article presents a machine learning based framework for classification and detection of leaf disease. SVM, Naïve Bayes and CNN are used in framework. Preprocessing is done using histogram equalization. For feature extraction, PCA algorithm is used.
- Is Part Of:
- Materials today. Volume 51:Part 8(2022)
- Journal:
- Materials today
- Issue:
- Volume 51:Part 8(2022)
- Issue Display:
- Volume 51, Issue 8, Part 8 (2022)
- Year:
- 2022
- Volume:
- 51
- Issue:
- 8
- Part:
- 8
- Issue Sort Value:
- 2022-0051-0008-0008
- Page Start:
- 2277
- Page End:
- 2280
- Publication Date:
- 2022
- Subjects:
- Machine learning -- Precision agriculture -- Classification -- Detection -- Leaf disease -- Preprocessing -- Feature extraction
Materials science -- Congresses -- Periodicals
620.1 - Journal URLs:
- http://www.sciencedirect.com/science/journal/22147853 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.matpr.2021.11.398 ↗
- Languages:
- English
- ISSNs:
- 2214-7853
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21163.xml