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 | AGRI AI |
|---|---|
| ABSTRACT | Agriculture plays a vital role in ensuring food security and economic stability, particularly in developing countries where small-scale farmers depend heavily on traditional farming practices. Soil quality assessment and crop disease identification are critical factors that directly influence agricultural productivity. However, conventional methods of soil testing and plant disease diagnosis are often time-consuming, costly, and dependent on expert intervention. Farmers in rural regions may not have immediate access to laboratory facilities or agricultural specialists, leading to delayed decision-making and reduced yield.This research presents AgriAI, a lightweight web-based agricultural assistance system designed to perform soil classification and crop health analysis using heuristic image-processing techniques. The system enables farmers to upload images of soil or crops, after which statistical image features such as RGB mean values, brightness intensity, and pixel variance are analyzed. Based on predefined rule-based classification logic, the system identifies soil type and provides preliminary crop health insights. The platform is developed using Python and Flask for backend processing and integrates image analysis libraries to ensure efficient performance without requiring GPU-based deep learning models.Experimental evaluation demonstrates that AgriAI provides fast response times, minimal hardware dependency, and practical usability in low-resource environments. The proposed system offers an accessible, cost-effective digital tool that bridges the gap between traditional farming methods and modern agricultural technology. |
| AUTHOR | K.ARUNA DEVI, S.S.SARAVANA KUMAR Student, Department of Computer Applications, Sri Ramakrishna College of Arts and Science, Coimbatore, India Assistant Professor, Department of Computer Applications, Sri Ramakrishna College of Arts & Science, Coimbatore, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403021 |
| pdf/21_AGRI AI.pdf | |
| KEYWORDS | |
| References | [1] S. Mohanty, D. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, 2016. https://doi.org/10.3389/fpls.2016.01419 [2] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, 2018. https://doi.org/10.1016/j.compag.2018.02.016 [3] J. G. A. Barbedo, “Digital image processing techniques for detecting, quantifying and classifying plant diseases,” SpringerPlus, 2013. https://doi.org/10.1186/2193-1801-2-660 [4] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018. https://www.pearson.com/en-us/subject-catalog/p/digital-image-processing/P200000003132 |
