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Main Author: Harshad Chaudhari
Format: Recurso digital
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Published: Zenodo 2025
Online Access:https://doi.org/10.5281/zenodo.14986802
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author Harshad Chaudhari
author_facet Harshad Chaudhari
contents <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>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_14986802
institution Zenodo
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publishDate 2025
publisher Zenodo
record_format zenodo
spellingShingle Advanced Machine Learning Technique to Detect Disease in Potato
Harshad Chaudhari
<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>
title Advanced Machine Learning Technique to Detect Disease in Potato
url https://doi.org/10.5281/zenodo.14986802