10.57647/amc.2026.100103

Feasibility of Using Artificial Intelligence and Big Data in Wheat Seed Production

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  • Farshad Habibi*1,2,
  1. Department of Agriculture, Miy.C. Islamic Azad University, Miyndoab, Iran
  2. Artificial Intelligence and Big Data Automation Research Center, Urmia Branch, Islamic Azad University, Urmia, Iran

Received: 2025-02-03

Revised: 2025-11-21

Accepted: 2025-11-23

Published in Issue 2026-06-30

Copyright (c) 2026 Farshad Habibi (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Habibi, F. (2026). Feasibility of Using Artificial Intelligence and Big Data in Wheat Seed Production. Agricultural Marketing and Commercialization, 10(1). https://doi.org/10.57647/amc.2026.100103
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Abstract

The integration of Artificial Intelligence (AI) and Big Data analytics is revolutionizing agriculture by en hancing productivity and sustainability. This study investigates the feasibility of applying these technologies to optimize wheat seed production, a critical component of food security. We analyzed a two-year dataset (2022-2024) of ‘Pishgam’ wheat variety yield from a commercial seed production company, covering four distinct cities (Urmia, Naghadeh, Piranshahr, Oshnaviyeh) in West Azerbaijan, Iran, with 11 farm locations sampled within each city. A two-way Analysis of Variance (ANOVA) was conducted to assess the effects of ‘Year’, ‘City’, and their interaction on seed yield. The results revealed significant main effects for both Year (p<0.001p < 0.001p<0.001) and City (p<0.001p < 0.001p<0.001). Crucially, a statistically significant interaction effect between Year and City was observed (p=0.043p = 0.043p=0.043), indicating that yield performance across cities varied non-uniformly between the two years. This complex spatio-temporal variability, influenced by fluctuating local environmental conditions, underscores the inadequacy of traditional predictive models. We conclude that the application of AI and Big Data technologies is not only feasible but essential for accurately forecasting yield, managing production risks, and developing resilient, location-specific agricultural strategies for wheat seed productio

Keywords

  • Yield Prediction,
  • Precision Agriculture,
  • Spatio-temporal Variability,
  • ANOVA,
  • Wheat Seed,
  • Big Data,
  • Artificial Intelligence
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