AI DRIVEN CROP DISEASE PREDICTION
Abstract & Details
Research Area
BTech Information Technology
Keywords
AI&ML
CNN
PYTHON.
Abstract
Agricultural productivity is significantly impacted by various crop diseases, leading to decreased yields and economic losses. Early and accurate detection of these diseases is crucial for timely intervention and management. This project aims to develop an AI-driven crop disease prediction and classification system using advanced deep learning techniques. By leveraging convolutional neural networks (CNNs) and integrating digital soil sensors, the system analyses images of plant leaves to identify specific diseases. The system also incorporates soil nutrient data to recommend optimal crops for specific soil conditions. Through image processing and predictive modelling, this project seeks to provide a scalable solution for farmers to diagnose diseases in real-time, improve crop management, and enhance agricultural sustainability. The proposed solution also explores the use of open-source tools such as TensorFlow and PyTorch, offering flexibility and affordability for widespread adoption.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Rhishub Kamalakar Gawade | DY Patil School of Engineering and Technology AMBI,PUNE |
| 2 | Sahil Sanjay Gorad | DY Patil School of Engineering and Technology AMBI,PUNE |
| 3 | Harsh Ramesh Bhosle | DY Patil School of Engineering and Technology AMBI,PUNE |
| 4 | Rajas Sunil Bengale | DY Patil School of Engineering and Technology AMBI,PUNE |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Gawade, Rhishub Kamalakar, Gorad, Sahil Sanjay, Bhosle, Harsh Ramesh, & Bengale, Rajas Sunil (2025). AI DRIVEN CROP DISEASE PREDICTION. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 2390-2395.
MLA Style
Gawade, Rhishub Kamalakar, et al. "AI DRIVEN CROP DISEASE PREDICTION." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 2390-2395.
IEEE Style
Rhishub Kamalakar Gawade, Sahil Sanjay Gorad, Harsh Ramesh Bhosle, and Rajas Sunil Bengale, "AI DRIVEN CROP DISEASE PREDICTION," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 2390-2395, 2025.
Vancouver Style
Gawade Rhishub Kamalakar, Gorad Sahil Sanjay, Bhosle Harsh Ramesh, Bengale Rajas Sunil. AI DRIVEN CROP DISEASE PREDICTION. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):2390-2395.
Harvard Style
Gawade, Rhishub Kamalakar, Gorad, Sahil Sanjay, Bhosle, Harsh Ramesh, & Bengale, Rajas Sunil (2025) 'AI DRIVEN CROP DISEASE PREDICTION', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 2390-2395.
Chicago Style
Gawade, Rhishub Kamalakar, et al. "AI DRIVEN CROP DISEASE PREDICTION." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 2390-2395.
Turabian Style
Gawade, Rhishub Kamalakar, et al. "AI DRIVEN CROP DISEASE PREDICTION." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 2390-2395.
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