The black-box nature of deep learning undermines
Research gap analysis derived from 4 agriculture papers in our local library.
The gap
The black-box nature of deep learning undermines transparency and farmer trust. The lack of interpretability in leaf disease classification limits the development of trustworthy AI systems.
Evidence profile
Sourced from the stated research gap of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 4 journals. Those papers have been cited 235 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 4 representative gaps
- 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 5/5Keywords: application wheat breeding remains fragmented limited integration across - Explainable AI for Precise Leaf Disease Diagnosis: A Comparative Study (2026) · Engineering Technology & Applied Science Research · doi
The black-box nature of deep learning undermines transparency and farmer trust. The lack of interpretability in leaf disease classification limits the development of trustworthy AI systems.
generalstated research gapevidence 5/5Keywords: black-box nature deep learning undermines transparency farmer trust - Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops (2024) · Scientific Reports · cited 58× · doi
The study identifies the need for creative thinking and interdisciplinary cooperation to overcome the obstacles to AI adoption in agriculture. The paper highlights the limitations of current machine learning and deep learning models in agriculture.
generalstated research gapevidence 5/5Keywords: study identifies need creative thinking interdisciplinary cooperation overcome - 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 5/5Keywords: growing demand transparency interpretability agricultural decision-making necessitates use
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