Enhancing Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health
- Title
- Enhancing Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health
- Creator
- Thirumeninathan V.; Vijayalakshmi S.; Palathara S.T.
- Description
- Detection and effective remedies play a crucial role in revolutionizing banana crop health. The banana industry faces numerous challenges, including the prevalence of diseases and pests that can lead to significant yield losses. This paper explores the potential impact of detection techniques and remedies on improving banana crop management. Disease detection models based on machine learning, image processing and deep learning offer high accuracy in identifying diseases like Fusarium Wilt, Yellow Sigatoka, and Black Sigatoka. Implementing detection and targeted treatments can enhance crop productivity, reduce pesticide usage, and ensure sustainable banana production. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolutional Neural Network; Deep Learning; Image processing; Machine Learning
- Coverage
- Thirumeninathan V., CHRIST(Deemed to Be University), Department of Data Science, Pune, India; Vijayalakshmi S., CHRIST(Deemed to Be University), Department of Data Science, Pune, India; Palathara S.T., CHRIST(Deemed to Be University), Department of Data Science, Pune, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Thirumeninathan V.; Vijayalakshmi S.; Palathara S.T., “Enhancing Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19164.