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Automating insect monitoring using unsupervised near-infrared sensors
Klas Rydhmer,Emily Bick,Laurence Still,Alfred Strand,Rubens Luciano,Salena Helmreich,Brittany Beck,Christoffer Grønne,Ludvig Malmros,Knud Poulsen,Frederik Elbæk,Mikkel Brydegaard,Jesper Lemmich,Thomas Nikolajsen +13 more
TLDR
In this paper, the authors present a network of distributed wireless sensors, recording backscattered near-infrared modulation signatures from insects, including wing beat harmonics, melanisation and flight direction.Abstract:
Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. However, conventional monitoring methods of trapping and identification are time consuming and thus expensive. Here, we present a network of distributed wireless sensors, recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value of 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional trapping.read more
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Emerging technologies revolutionise insect ecology and monitoring.
Roel van Klink,Tom August,Yves Bas,Paul Bodesheim,Aletta Bonn,Frode Fossøy,Toke T. Høye,Eelke Jongejans,Myles H. M. Menz,Andreia Miraldo,Tomas Roslin,Helen E. Roy,Ireneusz Ruczyński,Dmitry Schigel,Livia Schäffler,J. K. Sheard,Cecilie S. Svenningsen,Georg F. Tschan,J Wäldchen,Vera M. A. Zizka,Jens Åström,Diana E. Bowler +21 more
TL;DR: In this paper , the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods) for insect ecology and monitoring is described. And the potential for integration among different monitoring programs and technologies is discussed.
Journal ArticleDOI
A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added
TL;DR: In this article , the authors used the combination of artificial intelligence and agricultural technology to continuously and dynamically monitor pests in orchards, help scientific researchers and fruit farmers master pest data in time, reduce the use of artificial and pesticides, and achieve scientific early warning and prevention of pests.
Journal ArticleDOI
Automatic Pest Monitoring Systems in Apple Production under Changing Climatic Conditions
TL;DR: In this article , the authors summarize the automatic methods used to monitor the major pest in apple production (Cydia pomonella L.) and other important apple pests (Leucoptera maifoliella Costa, Grapholita molesta Busck, Halyomorpha halys Stål, and fruit flies) to improve sustainable pest management under frequently changing climatic conditions.
Journal ArticleDOI
Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities
Ioannis Saradopoulos,Ilyas Potamitis,Stavros Ntalampiras,Antonios I. Konstantaras,E Antonidakis +4 more
TL;DR: The paper compares different aspects of performance of three different edge devices, namely ESP32, Raspberry Pi Model 4 (RPi), and Google Coral, running a deep learning framework (TensorFlow Lite) and suggests that ESP32 appears to be the best choice in the context of this application.
Journal ArticleDOI
Insect biomass density: measurement of seasonal and daily variations using an entomological optical sensor
TL;DR: In this article , an infrared laser-based system is used to remotely monitor the biomass density of flying insects in the wild, and the average dry mass was 17.1 mg and the median 3.4 mg.
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