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 | An Integrated Web-Based Crop Management System Using Multi-Module Machine Learning for Precision Agricultural Advisory |
|---|---|
| ABSTRACT | Throughout South Asia, smallholder farmers continue to encounter a major information gap regarding how climate and soil interact with each other to influence crop production. As such, for many rural farmers, obtaining agronomic advice from an agronomist is often unfeasible since those agricultural specialists are often located too far away, and may also be too expensive. This paper presents an Integrated Crop Management System, (ICMS), which is an integrated web-based platform for providing farmers with multiple types of machine learning-based data, as well as the associated agronomic recommendations. This Integrated Crop Management System consists of five different components, each of which are capable of making decisions about specific crops: (i) A Random Forest classifier using Shannon entropy splitting which achieves 99.09 percent weighted accuracy across 22 crops that are commonly grown and use 7 soil and climatic measurements; (ii) A Gini Impurity Decision Tree which is based on 246,091 records of agricultural production at the district level across India that predicts what types of crops will grow in a district-state combination during any time of the year; () A Scikit-learn Label Encoded Decision Tree which recommends the amount and type of fertilizer to use for ten different fertilizer formulations at an overall accuracy of 92.6 percent with five-fold cross validation; (iv) From 115 years of archived observations collected by the Indian meteorological department, the mean monthly rainfall estimator has an average error of 18.4mm; (v) a profit and yield analytics engine built using Pandas computes state and season-specific projections of yield, market price, growing cost, and net returns. PHP controller scripts use secure command line calls to send user requests to self-contained Python scripts that return predicted values in accordance with the request. The ICMS has been compared with six systems currently available and has been found to provide better accuracy in crop recommendations than any of the other systems. In addition, all three types of advisory (agronomic, geographic, and economic) are combined into one easily accessible interface. |
| AUTHOR | KRUTHIKA K, KRUTHIKA M, MAANYA S, JYOTHI B T, GOPAYYA CHINTA Department of Computer Science and Engineering, CMR University, Bengaluru, Karnataka, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406043 |
| pdf/43_An Integrated Web-Based Crop Management System Using Multi-Module Machine Learning for Precision Agricultural Advisory.pdf | |
| KEYWORDS | |
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