Dynamics of pollen beetle (Brassicogethes aeneus) immigration and colonization of oilseed rape (Brassica napus) in Europe.

BACKGROUND: Understanding the dynamics of pest immigration into an agroecosystem enables effective and timely management strategies. The pollen beetle (Brassicogethes aeneus) is a primary pest of the inflorescence stages of oilseed rape (Brassica napus). This study investigated the spatial and temporal dynamics of pollen beetle immigration into oilseed rape fields in Denmark and the UK using multiple methods, including optical sensors. RESULTS: In all fields, pollen beetles were found to be aggregated and beetle density was related to plant growth stage, with more beetles occurring on plants after the budding stage than before inflorescence development. Optical sensors were the most efficient monitoring method, recording pollen beetles 2 and 4 days ahead of water traps and counts from plant scouting, respectively. CONCLUSION: Optical sensors are a promising tool for early warning of insect pest immigration. The aggregation pattern of pollen beetles post immigration could be used to precisely target control in oilseed rape crops. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Advances in automatic identification of flying insects using optical sensors and machine learning.

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.