The identification of pests is an arduous task

The gap

Evidence profile

Sourced from the stated research gap and future-work section of the source papers, classified as general, spanning 5 journals.

Supporting evidence — 6 representative gaps

• Editorial: Plant pest and disease model forecasting: enhancing precise and data-driven agricultural practices (2026) · Frontiers in Plant Science · doi

  Current methods have limitations, such as low accuracy and efficiency. There is a need for more accurate and efficient methods for plant pest and disease forecasting. The current gap is the lack of development and application of advanced models and algorithms for disease and pest detection.

  generalstated research gap
  Keywords: current methods have limitations low accuracy efficiency there
• AGRIGURU: A smart artificial intelligence solution for crop recommendation and plant disease detection (2026) · Plant Science Today · doi

  Current methods for disease detection have been slow-paced, prone to misinterpretation, and labour-intensive. There is a need for a system that can integrate plant disease detection, crop recommendation, and crop yield prediction.

  generalstated research gapevidence 5/5
  Keywords: current methods disease detection have been slow-paced prone
• BDL-Net: A blended deep learning approach for pest detection in agriculture using IoT-enabled sound analysis (2026) · Scientific Reports · doi

  The identification of pests is an arduous task that requires competent professionals. The existing models have limitations in detecting pests with high accuracy.

  generalstated research gapevidence 5/5
  Keywords: identification pests arduous task requires competent professionals existing
• AI-driven mulberry leaf disease detection for sustainable and resilient sericulture systems (2026) · Discover Sustainability · doi

  To extend the proposed model to other crop diseases - To compare the performance of the proposed model with other architectures - To evaluate the effectiveness of the proposed model in real-world scenarios

  generalfuture-work sectionevidence 5/5
  Keywords: extend proposed model other crop diseases compare performance
• Crop Yield Prediction and Disease Detection (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  Existing systems lack an integrated approach for disease detection and yield prediction. Traditional statistical methods are often insufficient for crop yield prediction.

  generalstated research gapevidence 5/5
  Keywords: existing systems lack integrated approach disease detection yield
• Deep Learning-Based Crop Disease Detection for Precision Agriculture - A Survey (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  There is a need for more accurate and reliable approaches for crop disease detection. Traditional methods of crop disease detection have limitations, and there is a gap in the current state of research.

  generalstated research gapevidence 5/5
  Keywords: there need accurate reliable approaches crop disease detection

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