Flórián Norbert, Jósvai Júlia Katalin, Tóth Zsolt, Gergócs Veronika, Sipőcz László, Tóth Miklós, Dombos Miklós
Institute for Soil Sciences, Centre for Agricultural Research, ELKH, Herman Ottó út 15, H-1022 Budapest, Hungary.
Plant Protection Institute, Centre for Agricultural Research, ELKH, Pf. 102, H-1525 Budapest, Hungary.
Insects. 2023 Apr 13;14(4):381. doi: 10.3390/insects14040381.
Monitoring insect populations is essential to optimise pest control with the correct protection timing and the avoidance of unnecessary insecticide use. Modern real-time monitoring practices use automatic insect traps, which are expected to be able to estimate the population sizes of pest animals with high species specificity. There are many solutions to overcome this challenge; however, there are only a few data that consider their accuracy under field conditions. This study presents an opto-electronic device prototype (ZooLog VARL) developed by us. A pilot field study evaluated the precision and accuracy of the data filtering using an artificial neural network(ANN) and the detection accuracy of the new probes. The prototype comprises a funnel trap, sensor-ring, and data communication system. The main modification of the trap was a blow-off device that prevented the escape of flying insects from the funnel. These new prototypes were tested in the field during the summer and autumn of 2018, detecting the daily and monthly flight of six moth species (, , , , , ). The accuracy of ANN was always higher than 60%. In the case of species with larger body sizes, it reached 90%. The detection accuracy ranged from 84% to 92% on average. These probes detected the real-time catches of the moth species. Therefore, weekly and daily patterns of moth flight activity periods could be compared and displayed for the different species. This device solved the problem of multiple counting and gained a high detection accuracy in target species cases. ZooLog VARL probes provide the real-time, time-series data sets of each monitored pest species. Further evaluation of the catching efficiency of the probes is needed. However, the prototype allows us to follow and model pest dynamics and may make more precise forecasts of population outbreaks.
监测昆虫种群对于在正确的保护时机优化害虫控制以及避免不必要的杀虫剂使用至关重要。现代实时监测方法使用自动昆虫诱捕器，期望能够以高物种特异性估计害虫动物的种群规模。有许多方法可以克服这一挑战；然而，只有少数数据考虑了它们在田间条件下的准确性。本研究展示了我们开发的一种光电设备原型（ZooLog VARL）。一项试点田间研究评估了使用人工神经网络（ANN）进行数据过滤的精度和准确性以及新探针的检测准确性。该原型包括一个漏斗诱捕器、传感器环和数据通信系统。诱捕器的主要改进是一个吹气装置，可防止飞行昆虫从漏斗中逃脱。这些新原型在2018年夏秋季节进行了田间测试，检测了六种蛾类物种（ 、 、 、 、 、 ）的每日和每月飞行情况。ANN的准确率始终高于60%。对于体型较大的物种，准确率达到了90%。检测准确率平均在84%至92%之间。这些探针检测到了蛾类物种的实时捕获情况。因此，可以比较并展示不同物种蛾类飞行活动期的每周和每日模式。该设备解决了多次计数的问题，并且在目标物种情况下获得了较高的检测准确率。ZooLog VARL探针提供了每个被监测害虫物种的实时时间序列数据集。需要进一步评估这些探针的捕获效率。然而，该原型使我们能够跟踪和模拟害虫动态，并可能对种群爆发做出更精确的预测。
