Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

在世界范围内，农民使用杀虫剂来防止虫害对作物造成的损害，同时他们还依靠昆虫传粉者来提高作物产量，而其他昆虫则是害虫的天敌。为了仅针对害虫使用杀虫剂，农民必须确切知道害虫和益虫出现在田间的位置和时间。这个问题的一个有前途的解决方案可能是结合机器学习的光学传感器。我们获得了大约 10,000 条在油菜（欧洲油菜）中发现的飞虫记录) 作物，使用光学遥感器并对获得的信号评估三种不同的分类方法，准确率达到 80% 以上。我们证明可以对飞行中的昆虫进行分类，从而可以在空间和时间上优化杀虫剂的应用。这将实现精准农业的技术飞跃，在精准农业中，谨慎使用对环境敏感的农药是重中之重。