Scutaru Daniela, Bergonzoli Simone, Costa Corrado, Violino Simona, Costa Cecilia, Albertazzi Sergio, Capano Vittorio, Kostić Marko M, Scarfone Antonio
Council for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, Italy.
Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Via di Corticella, 133, 40128 Bologna, Italy.
Insects. 2025 Jan 14;16(1):75. doi: 10.3390/insects16010075.
Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis.
养蜂是一项至关重要的农业活动，通过蜜蜂高效授粉显著促进环境健康和粮食生产。然而，蜜蜂面临诸多威胁，包括外来寄生虫、大规模运输以及可能增加寄生虫和病原体传播风险的常见农业做法。一个主要威胁是螨虫，它以蜜蜂的脂肪体为食并传播病毒，导致蜂群大量损失。检测这种寄生虫并确定有效治疗的干预阈值是一项困难且耗时的任务；现有的检测方法不同，但主要基于肉眼观察，导致准确性较低。本研究引入了一种结合人工智能算法（BeeVS）的数字便携式扫描仪，用于检测瓦螨。该设备通过对先前放置在蜂箱下方几天的粘虫板进行图像分析来工作，拦截自然掉落的瓦螨。在本研究中，该扫描仪进行了17周的测试，每周从5个蜂箱接收粘虫板，并将该方法的准确性、可靠性和速度与传统的人工目视检查进行比较。结果突出了测量的高重复性（R≥0.998）以及BeeVS设备的高精度；当每张粘虫板上至少有10只螨虫时，该设备的累积百分比误差低于1%，而人工目视观察的误差约为20%。鉴于其重复性和可靠性，该设备可被视为养蜂人和科学家的有效工具，与基于抽样的目视计数不同，它提供了在短时间内监测多个蜂箱的机会。
