International Journal of Machine Learning, AI & Data Science Evolution

E-ISSN: 3067-5073

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Call for Paper Volume 2 Issue 5 September-October 2026 Submit your research before last 3 days of this month to publish your research paper in the issue of September-October.
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Volume 2 Issue 5
September-October 2026

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Artificial Intelligence-Based Identification of Crop Yield Variations Using Agricultural and Weather Data

Author(s) Javier Martínez
Country Spain
Abstract Crop yield varies in response to complex interactions among weather conditions, soil properties, crop-management practices, and seasonal growing conditions. Conventional statistical approaches can identify important associations between selected variables and yield, but agricultural relationships are frequently nonlinear and may involve interactions that are difficult to represent through simple parametric equations. Artificial intelligence provides a methodological approach for combining heterogeneous agricultural and weather information and identifying patterns associated with high, moderate, or reduced crop productivity. This hypothetical methodological study examines artificial intelligence-based identification of crop-yield variation using agricultural and meteorological variables. A fictional dataset containing 3,200 field-season observations is divided into training, validation, and independent testing subsets. Predictor variables include cumulative rainfall, maximum and minimum temperature, growing-degree accumulation, solar radiation, soil-moisture index, soil organic matter, nitrogen input, irrigation frequency, planting-date deviation, and previous-season yield.
Multiple linear regression, artificial neural network, and random forest regression are compared. The illustrative analysis evaluates prediction through root mean squared error, mean absolute error, and coefficient of determination. Under the assigned hypothetical values, random forest achieves the strongest overall performance, followed by the artificial neural network. A second analysis evaluates the random-forest model across moisture-limited, near-normal, heat-stressed, and favorable agro-weather conditions. Prediction errors are larger under stress-related conditions, illustrating the greater uncertainty associated with extreme environmental combinations. The study concludes that artificial intelligence can support identification of crop-yield variations when agricultural-management variables and weather information are analyzed jointly. However, reliable prediction requires representative multi-season data, appropriate temporal and geographic validation, agronomically meaningful variables, careful treatment of extreme weather, and clear distinction between statistical association and causal crop response. All numerical findings in this paper are illustrative and require empirical validation with verified agricultural data.
Keywords Artificial Intelligence, Crop Yield Prediction, Agricultural Data, Weather Data, Machine Learning, Random Forest, Artificial Neural Network, Precision Agriculture, Agroclimatic Analytics, Yield Variability.
Field Engineering
Published In Volume 2, Issue 5, September-October 2026
Published On 2026-09-02
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E-ISSN: 3067-5073
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