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

2025-08-26

AI Summary

Addressing the dual challenges of “shadow bias”—caused by shadow interference in weed identification—and scarce labeled data in agricultural fields, this paper proposes a diagnosis-driven semi-supervised learning framework. First, interpretable analysis is employed to diagnose model misclassifications induced by shadows; then, an error-aware pseudo-labeling strategy is introduced to effectively leverage unlabeled data across ResNet, YOLO, and RF-DETR baselines. The approach significantly mitigates shadow bias while improving recall and robustness. On our custom agricultural dataset, it achieves a classification F1-score of 0.90 and detection mAP₅₀ of 0.82. Notably, it maintains strong performance under low-labeling budgets (e.g., <10% annotated samples), demonstrating practical viability for real-world deployment. This work delivers a cost-effective, reliable solution for precision agricultural spraying systems, bridging the gap between interpretability, data efficiency, and field applicability.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Detecting weeds under challenging field conditions with shadows
• Overcoming data scarcity through semi-supervised learning approach
• Addressing model bias that misidentifies shadows as vegetation

Innovation

• Semi-supervised framework using pseudo-labeling
• Diagnostic-driven approach to mitigate shadow bias
• Leveraging unlabeled data to enhance model robustness