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 | Smart Farm Assistant: A Web-Based System for Crop Disease Detection and Advisory |
|---|---|
| ABSTRACT | Agriculture plays a crucial role in sustaining livelihoods worldwide; however, crop diseases remain a significant challenge, often leading to reduced yield and economic loss for farmers. Early identification and effective management of plant diseases are essential but are not always accessible, particularly in rural areas. This paper presents a Smart Farm Assistant System developed using Python, Flask, and PostgreSQL. The system is implemented as a web-based application that provides structured information about crops, common diseases, and their preventive as well as corrective measures. Users can easily search for plant details and obtain relevant guidance through a simple and user-friendly interface. Unlike complex machine learning-based systems, the proposed solution emphasizes accessibility, low cost, and ease of use. The system assists farmers in making informed decisions, reduces dependency on agricultural experts, and promotes efficient farming practices. Furthermore, it provides a foundation for future enhancements such as AI-based disease detection and mobile application integration. |
| AUTHOR | MINAKSHI TAMBE, SAKSHI DIGHE Assistant Professor, Department of Computer Science, S.M.B.S.T. College, Sangamner, India Student, B.Sc. Department of Computer Science, S.M.B.S.T. College, Sangamner, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404146 |
| pdf/146_Smart Farm Assistant A Web-Based System for Crop Disease Detection and Advisory.pdf | |
| KEYWORDS | |
| References | 1. Shoaib, M., et al. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science. 2. Pacal, I., et al. (2024). A systematic review of deep learning techniques for plant diseases. Artificial Intelligence Review. 3. Sujatha, R., et al. (2025). Advancing plant leaf disease detection integrating machine learning and deep learning. Scientific Reports. 4. Elghawth, R., et al. (2025). Deep learning for transformer-based plant disease detection: A bibliometric analysis. MDPI Proceedings. 5. Kumar, R., & Singh, V. (2025). Advancing plant disease detection: A comparative analysis of deep learning and hybrid machine learning models. Machine Graphics and Vision. |
