International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | PLANTAECURE: Web-Based Plant Disease Detection and Remedy Recommendation System using Gemini AI |
|---|---|
| ABSTRACT | Agriculture is one of the most important sectors in India, but plant diseases continue to affect crop productivity and quality. Early detection of diseases is necessary to reduce losses, but traditional methods require expert knowledge and are not always accessible to farmers. This paper presents Plantaecure, a web-based plant disease detection and remedy recommendation system developed using Python, Flask, HTML, CSS, JavaScript, MySQL, and Google Gemini AI. The system allows users to upload plant leaf images through a simple website interface. The backend processes the image and sends it to the Google Gemini AI model using the Google GenAI API, which analyzes the image and identifies the disease. The system not only detects the disease but also provides remedies and prevention suggestions, making it more useful for real-world applications. The aim is to create a simple, accessible, and intelligent system that can be used by farmers, students, and plant lovers. |
| AUTHOR | JANHAVI KHOPADE, ADITI BADADE, AKANKSHA DANDALE, NARAYANI GOGAWALE, V.A. UPADHYE U.G. Students, Dept. of Computer Technology, BVJNIOT, Pune, Maharashtra, India Lecturer, Dept. of Computer Technology, BVJNIOT, Pune, Maharashtra, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403094 |
| pdf/94_PLANTAECURE Web-Based Plant Disease Detection and Remedy Recommendation System using Gemini AI.pdf | |
| KEYWORDS | |
| References | [1] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, 2016. [2] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, 2018. [3] D. Hughes and M. Salathé, “PlantVillage Dataset,” 2015. [4] TensorFlow Developers, “TensorFlow: Machine Learning Framework,” 2015. [5] J. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, 2019. [6] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. |
