Issue |
E3S Web Conf.
Volume 556, 2024
International Conference on Recent Advances in Waste Minimization & Utilization-2024 (RAWMU-2024)
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Article Number | 01028 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/e3sconf/202455601028 | |
Published online | 09 August 2024 |
Agri Watch: Precision Plant Health Monitoring using Deep Learning
Lovely Professional University, Phagwara Punjab 144411, India
* Corresponding author: ujjwalkrishna123321@gmail.com
The growth of deep learning technologies allows us to achieve higher accuracy in the classification of plant diseases, as well as in other domains. This research reveals the performance of several DL approaches, including custom convolutional neural networks (CNNs) and models which are pre-trained namely VGG16 and ResNet34, which were used for the recognition of diseases in plants that are depicted through the images. These models may obtain the necessary growing environment for training and assessing the models by using a publicly accessible dataset that includes pictures of both healthy and diseased plants, in total there are 14 unique plants used. The results of the experiment suggest that all the models combinedly gave 98.46% accuracy in the classification of diverse plant diseases. In addition to this, the paper discusses the hyperparameters like learning rate and optimizer choice that affect the model furthermore, the project discusses the methods involved in training deep learning models on GPU devices computationally speaking. Thereby, this project can be added to the field of agriculture vision by showing that deep learning methods are good for plant disease classification.
Key words: Accuracy / Hyperparameters / Optimizer / Crop Management / GPU Computing / Agriculture Vision
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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