Deep Learning and Machine Learning Based Efficient Framework for Image Based Plant Disease Classification and Detection
Abstract
Without agriculture, human existence would be inconceivable. A large percentage of the world's population relies on agriculture for their daily needs. In addition, it creates a big number of jobs in the area. Using traditional agricultural practices results in lower yields, which is the fault of farmers. Agriculture and allied sectors will continue to be critical to the economy's long-term growth and prosperity. Farming has a slew of challenges, including disease detection and control and crop monitoring and tracking. Farming with intelligence is a realistic option in many situations. Smart agriculture is now possible because to the internet of things and machine learning approaches. Computer vision, image processing, and machine learning techniques are used in the automated leaf disease diagnostic system to analyze photographs of diseased leaves. A farmer can make an educated choice regarding a plant illness thanks to automated disease detection equipment that speeds up the diagnostic process. A farmer had to first send the contaminated leaf to a pathology lab for confirmation of the illness, which was a tedious process. It is the purpose of this paper to propose a framework for the real-time classification of agricultural images. Crop disease pictures categorization and illness prediction are made easier using this system.
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
- Authors
- Nancy, P., Pallathadka, H., Naved, M., Kaliyaperumal, K., Arumugam, K., & Garchar, V.
- Affiliations
- Saveetha University (IN)
- Manipur International University (IN)
- Amity University (IN)
- Ambo University (ET)
- Karpagam Academy of Higher Education (IN)
- Junagadh Agricultural University (IN)
- Venue
- 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)
- Publisher
- IEEE
- Published
- 2022-03-04
- Pages
- 1-6
- OpenAlex
- W4223952886
- Cited by
- 29
- References
- 20
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 13 · 2022: 7
- FWCI
- 12.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Advanced Computing Technologies and Applications (ICACTA) (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- plant disease classification, plant leaf disease detection, diagnostic imaging, smart agriculture, crop monitoring, computer vision, deep learning, real-time image classification, agricultural image analysis
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