Abstract
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.
Highlights
Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests
Modern day agriculture entails a delicate balance between increasing crop production to accommodate an increasing population[1,2,3] while limiting the use of pesticides in order to reduce the development of resistance to insecticides and to reduce other negative side effects including affecting non-target organisms, environmental pollution and human health issues[4,5,6]
We focus on four of the most important pests: cabbage stem flea beetles (Psylliodes chrysocephala) the adults of which can cause complete crop failure due to feeding damage during establishment[14] and the larval mining within plant stems causes considerable damage and yield loss[12,15]; pollen beetles (Brassicogethes aeneus) which can reduce yield by 70% through feeding damage if no pesticides are used 16; cabbage seed weevils (Ceutorhynchus obstrictus) which can reduce the crop yield by up to 18% due to larval feeding on seeds[12]; and Brassica pod midges (Dasineura brassicae) the larvae of which cause pod shatter and losses of seed yield by up to 82%12
Summary
Farmers use insecticides to prevent crop damage caused by insect pests, while they rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. 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. The key to reach this goal is to optimize the use of pesticides to periods and areas where pests are present and other insects are least affected This inevitably involves recognition of the insects in the field. Tauc et al.[28] have described progress with a pan-and-tilt system able to detect insects in 3D, without identification of insect groups or species Such monitoring systems must be validated on known free flying insects, to link the signals to the presence of each species or species group. This is labour-intensive, and has previously only been conducted in a limited number of studies on disease vector insects[29,30], and insects in a m eadow[31]
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