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

• URL: http://arxiv.org/abs/2508.19511v1
• Date: Wed, 27 Aug 2025 01:55:47 GMT
• Title: Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity
• Authors: Alzayat Saleh, Shunsuke Hatano, Mostafa Rahimi Azghadi,
• Abstract summary: This study tackles both issues through a diagnostic-driven, semi-supervised framework.<n>We use a unique dataset of approximately 975 labeled and 10,000 unlabeled images of Guinea Grass in sugarcane.<n>Our work provides a clear and field-tested framework for developing, diagnosing, and improving robust computer vision systems.
• Score: 7.019137213828947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The automated management of invasive weeds is critical for sustainable agriculture, yet the performance of deep learning models in real-world fields is often compromised by two factors: challenging environmental conditions and the high cost of data annotation. This study tackles both issues through a diagnostic-driven, semi-supervised framework. Using a unique dataset of approximately 975 labeled and 10,000 unlabeled images of Guinea Grass in sugarcane, we first establish strong supervised baselines for classification (ResNet) and detection (YOLO, RF-DETR), achieving F1 scores up to 0.90 and mAP50 scores exceeding 0.82. Crucially, this foundational analysis, aided by interpretability tools, uncovered a pervasive "shadow bias," where models learned to misidentify shadows as vegetation. This diagnostic insight motivated our primary contribution: a semi-supervised pipeline that leverages unlabeled data to enhance model robustness. By training models on a more diverse set of visual information through pseudo-labeling, this framework not only helps mitigate the shadow bias but also provides a tangible boost in recall, a critical metric for minimizing weed escapes in automated spraying systems. To validate our methodology, we demonstrate its effectiveness in a low-data regime on a public crop-weed benchmark. Our work provides a clear and field-tested framework for developing, diagnosing, and improving robust computer vision systems for the complex realities of precision agriculture.

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