More research in agricultural machine learning

The gap

Evidence profile

Sourced from the future-work section and stated research gap and inline gaps of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 6 journals. Those papers have been cited 152 times in total.

Supporting evidence — 8 representative gaps

• Few-Shot Learning for Strawberry Variety Classification: A Prototypical Network-Based Approach (2026) · Firat University Journal of Experimental and Computational Engineering · doi

  Applying the proposed method to other agricultural products. Exploring other few-shot learning methods for agricultural image classification. Increasing the size and diversity of the dataset used for training.

  generalfuture-work sectionevidence 5/5
  Keywords: applying proposed method other agricultural products exploring few-shot
• Evaluation of groundwater quality using Water-Quality Index and Geographical Information System for human consumption and irrigation in Kanyakumari District, South India (2026) · Engineering Geology and Hydrogeology · doi

  Further studies can be conducted to evaluate the effectiveness of sustainable irrigation practices in improving groundwater quality. The use of other approaches, such as machine learning algorithms, can be explored to evaluate groundwater quality.

  generalfuture-work sectionevidence 5/5
  Keywords: further studies conducted evaluate effectiveness sustainable irrigation practices
• AI in Agriculture: Techniques and Applications (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  There is a need for more research in agricultural machine learning. The paper identifies a gap in the application of machine learning techniques to address complex agricultural issues.

  generalstated research gapevidence 5/5
  Keywords: there need research agricultural machine learning paper identifies
• AI in Agriculture: Techniques and Applications (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Future research should focus on developing more advanced machine learning techniques for agriculture. The application of machine learning to other areas of agriculture, such as livestock management, should be explored.

  generalfuture-work sectionevidence 5/5
  Keywords: future research focus developing advanced machine learning techniques
• Sugarcane Yield Estimation at Field Scale Using Time Series Data from LANDSAT 7 (2026) · Journal of the Indian Society of Remote Sensing · doi

  Application of the methodology to other crops and regions. Investigation of the use of other satellite images and sensors. Development of more advanced machine learning algorithms for crop yield estimation.

  generalfuture-work sectionevidence 5/5
  Keywords: application methodology other crops regions investigation use satellite
• Optimizing cover crop practices as a sustainable solution for global agroecosystem services (2024) · Nature Communications · cited 76× · doi

  The study identifies a research gap in the optimization of cover crop practices for sustainable agroecosystem services. Prior work has reported mixed results on the effects of cover crops on agroecosystem services. The study seeks to address this gap by using a comprehensive dataset and advanced statistical methods.

  generalstated research gapevidence 5/5
  Keywords: study identifies research gap optimization cover crop practices
• Optimizing cover crop practices as a sustainable solution for global agroecosystem services (2024) · Nature Communications · cited 76× · doi

  Future research should focus on the development of sustainable agricultural practices. The study suggests that further research is needed to optimize cover crop practices for different regions and climates. The use of machine learning methods and advanced statistical techniques should be explored further in the context of agricultural sustainability.

  generalfuture-work sectionevidence 4/5
  Keywords: future research focus development sustainable agricultural practices study
• Multitask ST-LSTM model based on UAV hyperspectral remote sensing for wheat yield prediction (2026) · Frontiers in Plant Science · doi

  Knowledge-guided machine learning with multivariate sparse data for crop growth modelling. In real field experiments, yield samples are usually concentrated in the middle- yield interval, whereas extremely high- or low-yield samples are relatively scarce.

  generalinline gapsevidence 4/5
  Keywords: yield samples knowledge guided machine learning multivariate sparse crop growth modelling real experiments usually concentrated

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