THE ROLE OF INTELLIGENT MODELS IN REPEATED CROP SELECTION AND YIELD PREDICTION UNDER AGRO-CLIMATIC CONDITIONS
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Abstract
In Uzbekistan, the growing demand for agricultural products makes efficient use of land and water an important task. One promising approach is repeated cropping, where a second crop is cultivated after the main crop within the same season. The selection of a suitable repeated crop depends on many interrelated factors, including temperature, precipitation, humidity, soil characteristics, irrigation availability and the vegetation period. This paper examines the role of intelligent models in repeated crop selection and yield prediction under agro-climatic conditions.
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References
National Statistics Committee of the Republic of Uzbekistan, “Number of Permanent Population of the Republic of Uzbekistan,” Tashkent, 2025.
Ministry of Economy and Finance of the Republic of Uzbekistan, “Analytical Report on the Fulfillment of Target Indicators in Agriculture, Forestry and Fisheries by the End of 2025,” Tashkent, 2026.
National Statistics Committee of the Republic of Uzbekistan, “Gross Value Added of Industries at Current Prices: Agriculture, Forestry and Fisheries,” SIAT Statistical Database, 2026.
Research Institute of Organic Agriculture (FiBL), “Global Organic Market: Unprecedented Growth in 2020—Organic Retail Sales Grew by 14 Billion Euros and Exceeded the 120-Billion-Euro Mark,” 2022.
T. van Klompenburg, A. Kassahun, and C. Catal, “Crop Yield Prediction Using Machine Learning: A Systematic Literature Review,” Computers and Electronics in Agriculture, vol. 177, art. 105709, 2020, doi: 10.1016/j.compag.2020.105709.
A. Chlingaryan, S. Sukkarieh, and B. Whelan, “Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review,” Computers and Electronics in Agriculture, vol. 151, pp. 61– 69, 2018, doi: 10.1016/j.compag.2018.05.012.
M. A. Jabed and M. A. A. Murad, “Crop Yield Prediction in Agriculture: A Comprehensive Review of Machine Learning and Deep Learning Approaches, with Insights for Future Research and Sustainability,” Heliyon, vol. 10, no. 24, art. e40836, 2024, doi: 10.1016/j.heliyon.2024.e40836.
S. Khaki and L. Wang, “Crop Yield Prediction Using Deep Neural Networks,” Frontiers in Plant Science, vol. 10, art. 621, 2019, doi: 10.3389/fpls.2019.00621.
S. Khaki, L. Wang, and S. V. Archontoulis, “A CNN-RNN Framework for Crop Yield Prediction,” Frontiers in Plant Science, vol. 10, art. 1750, 2020, doi:
3389/fpls.2019.01750. [10] A. K. Srivastava et al., “Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data,” Scientific Reports, vol. 12, art. 3215, 2022.
H. Afzal et al., “Incorporating Soil Information with Machine Learning for Crop Recommendation to Improve Agricultural Output,” Scientific Reports, vol. 15, art. 8560, 2025, doi: 10.1038/s41598-025-88676-z.
M. S. B. Alam, V. Esichaikul, A. Lameesa, S. F. Ahmed, and A. H. Gandomi, “An Approach for Crop Recommendation with Uncertainty Quantification Based on Machine Learning for Sustainable Agricultural Decision-Making,” Results in Engineering, vol. 26, art. 105505, 2025, doi: 10.1016/j.rineng.2025.105505.