This study addresses the demand for real-time detection of tomatoes and tomato flowers by agricultural robots deployed on edge devices in greenhouse environments. Under practical imaging conditions, object detection systems often face challenges such as large scale variations caused by varying camera distances, severe occlusion from plant structures, and highly imbalanced class distributions. These factors make conventional object detection approaches that rely on fully annotated datasets difficult to simultaneously achieve high detection accuracy and deployment efficiency. To overcome these limitations, this research proposes an active learning driven lightweight object detection framework, integrating data analysis, model design, and training strategy. First, the size distribution of objects in raw agricultural images is analyzed to redefine an operational target range, thereby improving learning stability under real-world conditions. Second, an efficient feature extraction module is incorporated to reduce computational cost, while a lightweight attention mechanism is introduced to enhance feature representation under multi-scale and occluded scenarios. Finally, an active learning strategy is employed to iteratively select high-information samples for annotation and training under a limited labeling budget, effectively improving the recognition performance of minority and small-object categories. Experimental results demonstrate that, while maintaining a low parameter count and inference cost suitable for edge-device deployment, the proposed method effectively improves the detection performance of tomatoes and tomato flowers in raw images. Under limited annotation conditions, the framework achieves an overall detection accuracy of 67.8% mAP, validating its practicality and feasibility for intelligent agricultural applications.

翻译：本研究针对温室环境中部署在边缘设备上的农业机器人对番茄及番茄花的实时检测需求展开。在实际成像条件下，目标检测系统常面临由相机距离变化引起的大尺度差异、植物结构造成的严重遮挡以及高度不平衡的类别分布等挑战。这些因素使得依赖全标注数据集的传统目标检测方法难以同时实现高检测精度与部署效率。为克服这些局限，本研究提出一种主动学习驱动的轻量化目标检测框架，整合了数据分析、模型设计与训练策略。首先，通过分析原始农业图像中目标的尺寸分布，重新定义操作目标范围，从而提升实际条件下的学习稳定性。其次，引入高效特征提取模块以降低计算成本，同时采用轻量级注意力机制增强多尺度及遮挡场景下的特征表征能力。最后，在有限标注预算下采用主动学习策略迭代选择高信息量样本进行标注与训练，有效提升少数类别及小目标的识别性能。实验结果表明，在保持适用于边缘设备部署的低参数量与推理成本的同时，所提方法显著提升了原始图像中番茄及番茄花的检测性能。在有限标注条件下，该框架实现了67.8% mAP的整体检测精度，验证了其在智慧农业应用中的实用性与可行性。