Ashwini S.S,
Shanta Kumar B Patil,
Ajay Kalburgi,
Arun Deshmukhmath,
Rakesh H.S,
Vinayak Bhat,
- Assistant Professor Department of CSE, Sai Vidya Institute of Technology Bengaluru India
- Professor & Head of the Department Department of CSE, Sai Vidya Institute of Technology Bengaluru India
- Student Department of CSE, Sai Vidya Institute of Technology Bengaluru India
- Student Department of CSE, Sai Vidya Institute of Technology Bengaluru India
- Student Department of CSE, Sai Vidya Institute of Technology Bengaluru India
- Student Department of CSE, Sai Vidya Institute of Technology Bengaluru India
Abstract
Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional Neural Network (CNN) algorithms have been remarkably successful in automating this procedure in recent years, providing accurate and efficient solutions. This review study offers a thorough summary of the developments, approaches, datasets, and difficulties in the field of plant leaf disease detection with CNN algorithms. It talks about the different CNN designs, pre-processing methods, data augmentation techniques, and transfer learning approaches used in the latest studies. The system typically involves several stages: image acquisition, preprocessing, feature extraction, disease classification, and treatment advice generation. The automated nature of this technology offers several advantages over traditional methods. It enables early detection of diseases, which can prevent widespread crop damage and reduce the need for broad-spectrum chemical treatments. This study also emphasizes the significance of deployment concerns, assessment criteria, and benchmark datasets for real-world application. This review attempts to give scholars and practitioners useful insights and recommendations for future research in this important field of agricultural technology by synthesizing the body of existing literature. Automated plant disease detection and treatment advisors using artificial intelligence hold great promise for transforming agriculture by improving productivity, sustainability, and food security.
Keywords: Plant pathology, leaf symptoms, image processing, machine learning, feature extraction
[This article belongs to International Journal of Cheminformatics(ijci)]
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| Volume | 01 |
| Issue | 02 |
| Received | May 8, 2024 |
| Accepted | June 25, 2024 |
| Published | July 18, 2024 |