Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{207002,
        author = {M Monika and G. T. Prasanna Kumari},
        title = {Real Time Predictive Analytics for Crop Prediction and Assortment Planning Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3632-3640},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207002},
        abstract = {The agriculture sector in India, which accounts for more than 50% of the workforce, is dealing with various challenges like climate variability, traditional farming methods, and the choice of crops. Farmers then continue to grow the same crop without knowing what other crop would be better adapted in the soil and thereby poor yield and soil degradation occurs. This paper proposes a real time crop prediction and assortment planning machine learning based predictive analytics system. Based on the user's state, district and season information, it automatically fetches temperature, humidity, and rainfall data from a backend weather API (OpenWeatherMap, IMD) data. Soil pH is not available from weather APIs, so is derived from a district wise soil pH lookup table or farmer enters the soil pH. Crop Recommendation Dataset consisting of 2200 samples, 22 class of crops and four parameters-temperature, humidity, pH, rainfall has been used to train and test two classifiers Decision Tree (DT) and Support Vector Machine (SVM). The accuracy, precision, recall and AUC value of the DT model was 92%, 90%, 91% and 0.94 respectively. The accuracy, precision, recall and AUC of the SVM model were 86%, 84%, 85% and 0.88 respectively. The system not only cuts out the manual weather data entry process, but also provides recommendations for the farmer to make regarding crops and aids in sustainable farming. The results show that machine learning can add a lot to agricultural decision making.},
        keywords = {Crop Prediction, Assortment Planning, Machine Learning, Decision Tree, Support Vector Machine, Weather Data Automation, Agricultural Analytics.},
        month = {July},
        }