Most conventional AI models do not consider the spatial-temporal and dynamic nature of agroecosystems

The gap

Evidence profile

Sourced from the conclusions and stated research gap and limitations section of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 5 journals. Those papers have been cited 177 times in total.

Supporting evidence — 6 representative gaps

• Applications, Challenges, and Prospects of Artificial Intelligence in Crop Production (2026) · Plants · doi

  However, the large-scale application of AI in the agricultural field still faces multiple challenges: regional bias, quality problems, and lack of standardization at the data level; insufficient model generalization, lightweight deployment, and interpretability at the technical level; digital divide, adaptability to smallholder farmers, and maintenance costs at the implementation level; and data privacy, algorithmic fairness, and imperfect regulatory systems at the ethical and policy level.

  generalconclusions
  Keywords: level large scale application agricultural field still faces multiple challenges regional bias quality problems lack
• AI-Powered Sustainable Farming Assistant: A Conceptual Framework Using Machine Learning (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  The widespread unavailability of expert-level agronomic guidance at the point of need is a significant challenge. Many existing systems are too expensive or complex for small-scale farmers. There is a need for a software-based system that can provide reliable guidance for farmers.

  generalstated research gapevidence 5/5
  Keywords: widespread unavailability expert-level agronomic guidance point need significant
• A review of AI-driven phenomics, genomics, and predictive breeding in wheat (2026) · Discover Plants · doi

  The application of AI and ML in wheat breeding remains fragmented, with limited integration across phenotyping, genotyping, and multi-omics layers. There is a need for better model transparency and access to computing resources in developing regions.

  generalstated research gapevidence 5/5
  Keywords: application wheat breeding remains fragmented limited integration across
• A review of AI-driven phenomics, genomics, and predictive breeding in wheat (2026) · Discover Plants · doi

  The need for better model transparency and access to computing resources in developing regions is a significant limitation. The power imbalance between farmers and technology providers is a critical concern. Smallholder and resource-limited farmers are particularly disadvantaged due to high costs and limited digital infrastructure.

  generallimitations sectionevidence 5/5
  Keywords: need better model transparency access computing resources developing
• AI-driven hybrid framework for enhanced pest detection and resource optimization using graph networks and deep reinforcement learning (2026) · Scientific Reports · doi

  Most conventional AI models do not consider the spatial-temporal and dynamic nature of agroecosystems. Traditional AI face challenges in solving problems including real-time optimization of resources, adaptation to environments, and fusion of data.

  generalstated research gapevidence 5/5
  Keywords: conventional models consider spatial-temporal dynamic nature agroecosystems traditional
• Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making (2024) · Neural Computing and Applications · cited 177× · doi

  The growing demand for transparency and interpretability in agricultural decision-making necessitates the use of explainable artificial intelligence (XAI). The lack of accurate predictive models to anticipate the effect of climate change on crop yields.

  generalstated research gapevidence 5/5
  Keywords: growing demand transparency interpretability agricultural decision-making necessitates use

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