DESIGN AND IMPLEMENTATION OF A WEB-BASED CROP YIELD FORECASTING PLATFORM
Keywords:
Crop yield prediction; regression algorithms; machine learning; web-based platform; agricultural forecasting; smart agriculture; Uzbekistan.Abstract
Agriculture in Uzbekistan is facing new challenges due to climate variability and the growing need for efficient resource management. Predicting crop yields with higher precision can help farmers and researchers plan production more effectively and ensure food security. In this work, we present the design and implementation of MLR Predictor, a web-based platform developed to forecast grain crop yields using regression-based machine learning models. The system combines historical data on weather, precipitation, and vegetation conditions collected over several years to train and test predictive algorithms. Users can enter different environmental parameters directly on the website and instantly obtain forecast results. Several regression methods were applied, including Linear Regression, Random Forest, and XGBoost, to identify which algorithm provides the best generalization performance. The results showed that the Random Forest model can predict yields with an R² value close to 0.9 or higher, depending on the quality of the input data and region-specific features. The developed platform demonstrates how machine learning can be applied in agriculture to make data more useful and accessible. It serves as a practical decision-support tool and contributes to the ongoing development of smart agriculture and digital innovation in Uzbekistan.
References
D.R.Xasanov, M.Tojiyev, O.D.Primqulov. Gradient descent in machine learning.// IEEE International Conference on Information Science and Communications Technologies: applications, trends and opportunities (ICISCT-2021). https://ieeexplore.ieee.org/document/9670169
Rakhimov N.O., Xasanov D.R. Selection of Crops by Region using Machine Learning Classification Algorithms. // Ilm-fan va innovatsion rivojlanish jurnali. Volume 7, Issue 2; April 2024, -p. 36-47. https://cyberleninka.ru/article/n/selection-of-crops-by-region-using-machine-learning-classification-algorithms
N.Rahimov, D.Khasanov. The Implementation of Machine Learning and Deep Learning Algorithms for Crop Yield Prediction in Agriculture. // Bulletin of TUIT: Management and Communication Technologies, Volume 2, No. 2023. https://uzjurnal.uz/2/2023/2/index?issue=9
N.Rakhimov, D.Khasanov, H.Abdulhakimov. Prediction the Yield of Grain Crops using Basic Machine Learning Algorithms. // Raqamli transformatsiya va sun’iy intellekt ilmiy jurnali, Volume 2, Issue 5. https://dtai.tsue.uz/index.php/dtai/article/view/v2i57
N.Raximov, J.Kuvandikov, D.Khasanov, “The importance of loss function in artificial intelligence”, International Conference on Information Science and Communications Technologies (ICISCT 20222), DOI: 10.1109/ICISCT55600.2022.10146883
Rakhimov N.O., Khasanov D.R., Xafizadinov U.T. Application of the Algorithm for Enrichment the Knowledge Graph with Numerical Predicates in Decision-Making Support Systems. // Digital Transformation and Artificial Intelligence: Problems, Innovations and Trends 1st International Scientific-Practical Conference. https://dtai.tsue.uz/index.php/DTAI2024/article/view/dilmurot2
Rakhimov N.O., Khasanov D.R., Shirinboyev R.Sh., Xafizadinov U.T. Evaluating the Performance of Convolutional Neural Networks and Hybrid CNN-SVM Models for Symbol Recognition in Complex Datasets. // Digital Transformation and Artificial Intelligence: Problems, Innovations and Trends 1st International Scientific-Practical Conference. https://dtai.tsue.uz/index.php/DTAI2024/article/view/shirinbayev2
Rakhimov N.O., Khasanov D.R. The Implementation of Decision Tree Algorithm in Data Mining. // International Scientific Journal Science and Innovation, Special Issue: “Digital Technologies: Problems and Solutions of Practical Implementation in the Spheres”, 2023
N.O.Rakhimov, D.R.Khasanov. Selection of crops to be planted according to climate and soil characteristics with the help of machine learning classification algorithms. // The 10th International Scientific and Practical Conference “Global and Regional Aspects of Suistainable Development”, Copenhagen-2024. https://www.neliti.com/publications/628198/selection-of-crops-to-be-planted-according-to-climate-and-soil-characteristics-w
O.D.Primqulov, M.R.Tojiyev, D.R.Khasanov. Image segmentation in openCV and python. // ACADEMICIA. An International Multidisciplinary Research Journal (ISSN 2249-7137). Volume 10, Issue 12. December
Rahimov Nodir, Khasanov Dilmurod. (2022). The Mathematical Essence Of Logistic Regression For Machine Learning. https://doi.org/10.5281/zenodo.7239169
N.Raximov, O.Primqulov, B.Daminova,“Basic concepts and stages of research development on artificial intelligence”, International Conference on Information Science and Communications Technologies (ICISCT), www.ieeexplore.ieee.org/document/9670085/metrics#metrics
Ma’ruf Tojiyev, Ravshan Shirinboyev, Jahongirjon Bobolov. Image Segmentation By Otsu Method. International Journal of Contemporary Scientific and Technical Research, (Special Issue), 2023. 64–72, https://zenodo.org/record/7630893
T. Maruf, “Hazard recognition system based on violation of the integrity of the field and changes in the intensity of illumination on the video image,” in 2022 International Conference on Information Science and Communications Technologies, ICISCT 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICISCT55600.2022.10146933.
B. Z. Parmanov, F. M. Nazarov, and M. R. Tojiyev, “Development of an Algorithm for Intelligent Analysis of Data From a Microprocessor-Based Portable Spectrophotometer,” in Proceedings - 2025 International Russian Smart Industry Conference, SmartIndustryCon 2025, Institute of Electrical and Electronics Engineers Inc., 2025, pp. 664–669. doi: 10.1109/SmartIndustryCon65166.2025.10985982.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
