Manish Dubey
Rishita Aloria
Prakshal Rohatgi
Tanya Arora
Pranshul Jain
- Professor, Department of Computer Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
Abstract
Growing healthy and productive crops is crucial in the global battle for food security. To minimize crop losses and apply timely control measures, early and precise diagnosis of plant diseases is essential. Conventional illness detection techniques are subjective, labor-intensive, and complicated; they frequently rely on eye inspection. The TensorFlow and OpenCV libraries are used in this study to explore the use of Convolutional Neural Networks (CNNs) for plant disease discovery. The suggested method uses CNNs’ ability to automatically identify distinguishing characteristics from leaf photos to classify diseases. An extensive dataset of photos of plant leaves, including both healthy and damaged leaves from different plant species, was gathered and pre-processed with the use of image augmentation methods to improve the robustness of the model. Carefully crafted CNN. TensorFlow was used to create the model, which used fully connected layers for disease classification and convolutional and pooling layers for feature extraction. After undergoing extensive training and evaluation, the model was able to classify different plant diseases with an accuracy of 94.2%. In addition to proving that CNNs are a useful tool for identifying plant diseases, this study investigates possible uses in the future, such as real-time disease diagnosis in mobile apps and integration with crop breeding initiatives to produce crops resistant to disease.
Keywords: Plant disease detection, Convolutional Neural Networks (CNNs), TensorFlow, OpenCV, Deep learning, Image classification, Precision agriculture, Sustainable agriculture.
[This article belongs to Journal of Electronic Design Technology(joedt)]
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References
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Journal of Electronic Design Technology
| Volume | 15 |
| Issue | 01 |
| Received | April 25, 2024 |
| Accepted | May 27, 2024 |
| Published | June 13, 2024 |