Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration. 

http://www.cell.com/article/S0169534722001343/pdf

Emerging technologies revolutionise insect ecology and monitoring.

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

Apple is one of the most important economic fruit crops in the world. Despite all the strategies of integrated pest management (IPM), insecticides are still frequently used in its cultivation. In addition, pest phenology is extremely influenced by changing climatic conditions. The frequent spread of invasive species, unexpected pest outbreaks, and the development of additional generations are some of the problems posed by climate change. The adopted strategies of IPM therefore need to be changed as do the current monitoring techniques, which are increasingly unreliable and outdated. The need for more sophisticated, accurate, and efficient monitoring techniques is leading to increasing development of automated pest monitoring systems. In this paper, we summarize the automatic methods (image analysis systems, smart traps, sensors, decision support systems, etc.) 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—Tephritidae and Drosophilidae) to improve sustainable pest management under frequently changing climatic conditions.

/pdf/automatic-pest-monitoring-systems-in-apple-production-under-2fvfc6n0.pdf

Automatic Pest Monitoring Systems in Apple Production under Changing Climatic Conditions

Our aim is to promote the widespread use of electronic insect traps that report captured pests to a human-controlled agency. This work reports on edge-computing as applied to camera-based insect traps. We present a low-cost device with high power autonomy and an adequate picture quality that reports an internal image of the trap to a server and counts the insects it contains based on quantized and embedded deep-learning models. 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). All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption. Our findings suggest that ESP32 appears to be the best choice in the context of this application according to our policy for low-cost devices.

/pdf/edge-computing-for-vision-based-urban-insects-traps-in-the-194fbm5o.pdf

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

Insects are major actors in Earth's ecosystems and their recent decline in abundance and diversity is alarming. The monitoring of insects is paramount to understand the cause of this decline and guide conservation policies. In this contribution, an infrared laser-based system is used to remotely monitor the biomass density of flying insects in the wild. By measuring the optical extinction caused by insects crossing the 36-m long laser beam, the Entomological Bistatic Optical Sensor System used in this study can evaluate the mass of each specimen. At the field location, between July and December 2021, the instrument made a total of 262,870 observations of insects for which the average dry mass was 17.1 mg and the median 3.4 mg. The daily average mass of flying insects per meter cube of air at the field location has been retrieved throughout the season and ranged between near 0 to 1.2 mg/m3. Thanks to its temporal resolution in the minute range, daily variations of biomass density have been observed as well. These measurements show daily activity patterns changing with the season, as large increases in biomass density were evident around sunset and sunrise during Summer but not during Fall. 

https://link.springer.com/content/pdf/10.1007/s00340-023-07973-5.pdf

Insect biomass density: measurement of seasonal and daily variations using an entomological optical sensor

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.

Brittany Beck

Papers

Automating insect monitoring using unsupervised near-infrared sensors