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| Natura: | Recurso digital |
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Zenodo
2025
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| Accesso online: | https://doi.org/10.5281/zenodo.14986802 |
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Sommario:
- <p>This research paper presents a deep learning-based approach for detecting diseases in potato plants using Convolutional Neural Networks (CNNs). The model is trained on a dataset of healthy and diseased potato leaf images to improve early disease detection and assist farmers with crop protection. </p> <p> Key Highlights: <br>✅ Machine Learning Model: CNNs for image classification <br>✅ Technologies Used: TensorFlow, FastAPI, ReactJS, React Native <br>✅ Deployment: Web & Mobile-based disease detection system <br>✅ Outcome: Achieved 95% accuracy in classifying potato leaf diseases </p> <p>This research contributes to the **automation of agriculture** by providing a scalable and accessible AI-powered solution for early disease detection. </p>