Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy.
Instar Biologicals, Yakima, WA, USA.
Pest Manag Sci. 2021 Sep;77(9):4084-4090. doi: 10.1002/ps.6433. Epub 2021 May 14.
Captures of codling moth, Cydia pomonella (L.), in traps are used to establish action thresholds and time insecticide sprays. The need for frequent trap inspections in often remote orchards has created a niche for remote sensing smart traps. A smart trap baited with a five-component pheromone-kairomone blend was evaluated for codling moth monitoring among an assemblage of other nontargets in apple and pear orchards.
Codling moth captures did not differ between the smart trap and a standard trap when both were checked manually. However, the correlation between automatic and manual counts of codling moth in the smart traps was low, R = 0.66 ÷ 0.87. False-negative identifications by the smart trap were infrequent <5%, but false-positive identifications accounted for up to 67% of the count. These errors were primarily due to the misidentification of three moth species of fairly similar-size to codling moth: apple clearwing moth Synanthedon myopaeformis (Borkhausen), oriental fruit moth Grapholita molesta (Busck), and carnation tortrix Cacoecimorpha pronubana (Hübner). Other false-positive counts were less frequent and included the misidentifications of dipterans, other arthropods, patches of moth scales, and the double counting of some moths.
Codling moth was successfully monitored remotely with a smart trap baited with a nonselective sex pheromone-kairomone lure, but automatic counts were inflated in some orchards due to mischaracterizations of primarily similar-sized nontarget moths. Improved image-identification algorithms are needed for smart traps baited with less-selective lures and with lure sets targeting multiple species.
苹果蠹蛾（Cydia pomonella（L.））在诱捕器中的捕获量被用于确定防治指标和施药时间。由于需要经常检查偏远果园中的诱捕器，因此为远程遥感智能诱捕器创造了一个利基市场。一种用五组分信息素-引诱剂混合物诱饵的智能诱捕器，在苹果和梨园的其他非目标物组合中，用于监测苹果蠹蛾。
当手动检查时，智能诱捕器和标准诱捕器的苹果蠹蛾捕获量没有差异。然而，智能诱捕器中自动和手动计数的苹果蠹蛾之间的相关性较低，R=0.66/0.87。智能诱捕器偶尔会出现错误识别，错误率低于 5%，但错误识别率高达 67%。这些错误主要是由于与苹果蠹蛾大小相似的三种蛾类的错误识别：苹果透翅蛾 Synanthedon myopaeformis（Borkhausen）、东方果实蛾 Grapholita molesta（Busck）和康乃馨卷叶蛾 Cacoecimorpha pronubana（Hübner）。其他错误识别的计数频率较低，包括双翅目昆虫、其他节肢动物、蛾类鳞片的斑块和一些蛾类的重复计数。
用非选择性性信息素-引诱剂混合物诱饵的智能诱捕器成功地远程监测了苹果蠹蛾，但由于主要是大小相似的非目标蛾类的错误特征化，一些果园中的自动计数会偏高。需要改进图像识别算法，以便为用非选择性诱饵和针对多种物种的诱捕器设置的智能诱捕器提供更好的监测效果。
