Further development of multi-agent AI systems for soil

The gap

Evidence profile

Sourced from the stated challenges and abstract and future-work section and stated research gap of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 136 times in total.

Supporting evidence — 7 representative gaps

• Estimating Soil Attributes for Yield Gap Reduction in Africa Using Hyperspectral Remote Sensing Data with Artificial Intelligence Methods: An Extensive Review and Synthesis (2025) · Remote Sensing · cited 24× · doi

  Low soil fertility and poor soil quality are significant challenges in African agriculture. The application of high-resolution remote sensing sensors for soil property mapping in Africa is underexplored. The combined application of AI approaches and hyperspectral remote sensing technologies for soil attribute estimation remains underexplored in the African context.

  generalstated challenges
  Keywords: low soil fertility poor quality significant challenges african
• Estimating Soil Attributes for Yield Gap Reduction in Africa Using Hyperspectral Remote Sensing Data with Artificial Intelligence Methods: An Extensive Review and Synthesis (2025) · Remote Sensing · cited 24× · doi

  , hyperspectral satellite sensors) for soil property mapping in Africa; (ii) there is a considerable value in AI approaches for estimating and mapping soil attributes, with a strong recommendation to further explore the potential of deep learning techniques; (iii) despite advancements in AI-based methodologies and the availability of hyperspectral sensors, their combined application remains underexplored in the African context.

  generalabstract
  Keywords: hyperspectral sensors soil mapping satellite property africa there considerable value approaches estimating attributes strong recommendation
• Potential of EnMAP Hyperspectral Imagery for Regional-Scale Soil Organic Matter Mapping (2025) · Remote Sensing · cited 17× · doi

  Future research should focus on evaluating EnMAP’s performance using advanced machine learning techniques and comparing it to other available hyperspectral products to establish robust protocols for satellite-based soil monitoring.

  generalabstract
  Keywords: future focus evaluating enmap performance using advanced machine learning techniques comparing available hyperspectral products establish
• Spatial Estimation of Soil Organic Matter and Total Nitrogen by Fusing Field Vis–NIR Spectroscopy and Multispectral Remote Sensing Data (2025) · Remote Sensing · cited 10× · doi

  Further research is needed to explore the application of multispectral satellite data for soil property estimation, - The use of other satellite data such as Sentinel data should be explored, - The development of new spectral indices for soil property estimation should be investigated

  generalfuture-work section
  Keywords: further research needed explore application multispectral satellite data
• Enhancing soil science research with multi-agent artificial intelligence systems (2026) · Frontiers in Science · cited 2× · doi

  Further development of multi-agent AI systems for soil science research. Application of the approach to other areas of soil science. Integration with other technologies such as remote sensing and IoT.

  generalfuture-work sectionevidence 5/5
  Keywords: further development multi-agent systems soil science research application
• Spatial Mapping of Cropland Soil Load Bearing Capacity in Northeastern Bangladesh: A Multi-Feature-based Prediction using Machine Learning-Remote Sensing Fusion (2026) · Earth Systems and Environment · doi

  There is a need for accurate prediction methods for soil load bearing capacity. Prior work has highlighted the importance of soil load bearing capacity in agricultural contexts, but there is a lack of studies using machine learning and remote sensing fusion. The study aims to address this gap by using a multi-feature-based approach with machine learning and remote sensing fusion.

  generalstated research gapevidence 5/5
  Keywords: there need accurate prediction methods soil load bearing
• Advancements and Perspective in the Quantitative Assessment of Soil Salinity Utilizing Remote Sensing and Machine Learning Algorithms: A Review (2024) · Remote Sensing · cited 59× · doi

  The influence of hidden environmental and confounding factors within the vegetation response spectra cannot be completely eliminated. The capability of vegetation indices to estimate salinity is often inadequate, leading to increased uncertainty in soil salinity inversion results. Deep learning algorithms remain underexplored in soil salinity monitoring.

  generalstated research gapevidence 5/5
  Keywords: influence hidden environmental confounding factors within vegetation response

Questions about this gap