Advances in automatic identifcation 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 fight, 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

CW-laser radar for combustion diagnostics

A CW-laser radar system developed for combustion diagnostics is described. It is based on triangulation to attain range information. Some initial results from measurements in sooting flames are shown and some future perspectives are discussed

Applications of KHZ-CW Lidar in Ecological Entomology

The benefits of kHz lidar in ecological entomology are explained. Results from kHz-measurements on insects, carried out with a CW-lidar system, employing the Scheimpflug principle to obtain range resolution, are presented. A method to extract insect events and analyze the large amount of lidar data is also described

Lidar profiling biological targets : - Detection limits and dynamic range.

Here we present recent advances and applications of profiling biological targets with Scheimpflug lidar. In particular we demonstrate applications for profiling the aerofauna and classifying various groups of species. Based on lidar data over the Ivorian countryside we investigate range dependent detection limits by comparing data with distinct sample rates and pulse energies