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This research aims to develop a semi-supervised framework to improve weed detection amidst environmental challenges and data scarcity.

March 8, 2026ComputersOpen Access

Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity

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This research aims to develop a semi-supervised framework to improve weed detection amidst environmental challenges and data scarcity.
Utilized a dataset of 975 labelled and 10,000 unlabelled images of Guinea Grass
Established strong supervised baselines using ResNet for classification and YOLO for detection
Implemented interpretability tools to uncover shadow bias
Developed a semi-supervised pipeline to enhance model robustness through pseudo-labelling
Achieved F1 scores up to 0.90 in weed classification
Obtained mAP50 scores exceeding 0.82 in detection tasks
Demonstrated effectiveness in low-data scenarios on a public crop–weed benchmark
Improved recall in weed detection for automated spraying systems

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Implication

This framework enhances weed detection using deep learning models in agriculture, highlighting shadow bias and improving precision agriculture outcomes.

Cite This Study

Saleh et al. (2026) studied this question.

synapsesocial.com/papers/69acc58f32b0ef16a404fd99 https://doi.org/10.3390/computers15030171

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Implication

This framework enhances weed detection using deep learning models in agriculture, highlighting shadow bias and improving precision agriculture outcomes.

Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity

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Discussion

Implication

Cite This Study

Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity

Key Points

Discussion

Implication

Cite This Study

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