置信计数：水诱捕器中害虫的精准监测 (Counting with Confidence: Accurate Pest Monitoring in Water Traps)

from arxiv, Accepted to AGRICONTROL 2025. \c{opyright} 2025 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. Published in IFAC-PapersOnLine, DOI: 10.1016/j.ifacol.2025.11.792

Accurate pest population monitoring and tracking their dynamic changes are crucial for precision agriculture decision-making. A common limitation in existing vision-based automatic pest counting research is that models are typically evaluated on datasets with ground truth but deployed in real-world scenarios without assessing the reliability of counting results due to the lack of ground truth. To this end, this paper proposed a method for comprehensively evaluating pest counting confidence in the image, based on information related to counting results and external environmental conditions. First, a pest detection network is used for pest detection and counting, extracting counting result-related information. Then, the pest images undergo image quality assessment, image complexity assessment, and pest distribution uniformity assessment. And the changes in image clarity caused by stirring during image acquisition are quantified by calculating the average gradient magnitude. Notably, we designed a hypothesis-driven multi-factor sensitivity analysis method to select the optimal image quality assessment and image complexity assessment methods. And we proposed an adaptive DBSCAN clustering algorithm for pest distribution uniformity assessment. Finally, the obtained information related to counting results and external environmental conditions is input into a regression model for prediction, resulting in the final pest counting confidence. To the best of our knowledge, this is the first study dedicated to comprehensively evaluating counting confidence in counting tasks, and quantifying the relationship between influencing factors and counting confidence through a model. Experimental results show our method reduces MSE by 31.7% and improves R2 by 15.2% on the pest counting confidence test set, compared to the baseline built primarily on information related to counting results.

翻译：精准监测害虫种群数量并追踪其动态变化对于精准农业决策至关重要。现有基于视觉的自动害虫计数研究普遍存在一个局限：模型通常在具有真实标注的数据集上进行评估，但在实际部署时，由于缺乏真实标注，无法评估计数结果的可靠性。为此，本文提出了一种基于计数结果相关信息和外部环境条件，全面评估图像中害虫计数置信度的方法。首先，利用害虫检测网络进行害虫检测与计数，提取计数结果相关信息。随后，对害虫图像进行图像质量评估、图像复杂度评估和害虫分布均匀性评估，并通过计算平均梯度幅值量化图像采集过程中因搅拌导致的图像清晰度变化。值得注意的是，我们设计了一种假设驱动的多因素敏感性分析方法，以选择最优的图像质量评估和图像复杂度评估方法，并提出了一种自适应DBSCAN聚类算法用于害虫分布均匀性评估。最后，将获取的计数结果相关信息和外部环境条件输入回归模型进行预测，得到最终的害虫计数置信度。据我们所知，这是首个专注于全面评估计数任务中置信度，并通过模型量化影响因素与计数置信度之间关系的研究。实验结果表明，与主要基于计数结果相关信息构建的基线方法相比，我们的方法在害虫计数置信度测试集上，均方误差（MSE）降低了31.7%，决定系数（R2）提高了15.2%。