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Integrated pest management relies on insect pest monitoring to support the decision of counteracting a given level of infestation and to select the adequate control method. The classic monitoring approach of insect pests is based on placing in single infested areas a series of traps that are checked by human operators on a temporal basis. This strategy requires high labor cost and provides poor spatial and temporal resolution achievable by single operators. The adoption of image sensors to monitor insect pests can result in several practical advantages. The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps. The use of software and image recognition algorithms can support automatic trap usage to identify and/or count insect species from pictures. Considering the high image resolution achievable and the opportunity to exploit data transfer systems through wireless technology, it is possible to have remote control of insect captures, limiting field visits. The availability of real-time and on-line pest monitoring systems from a distant location opens the opportunity for measuring insect population dynamics constantly and simultaneously in a large number of traps with a limited human labor requirement. The actual limitations are the high cost, the low power autonomy and the low picture quality of some prototypes together with the need for further improvements in fully automated pest detection. Limits and benefits resulting from several case studies are examined with a perspective for the future development of technology-driven insect pest monitoring and management.

Aedes aegypti is a mosquito species known for its role in transmitting dengue fever, a viral disease prevalent in tropical and subtropical regions. Recognizable by its white markings and preference for urban habitats, this mosquito breeds in standing water near human dwellings. A promising approach to combat the proliferation of mosquitoes is the use of smart traps, equipped with advanced technologies to attract, capture, and monitor them. The most significant results include 97% accuracy in detecting Aedes aegypti, 100% accuracy in identifying bees, and 90.1% accuracy in classifying butterflies in the laboratory. Field trials successfully validated and identified areas for continued improvement. The integration of technologies such as Internet of Things (IoT), cloud computing, big data, and artificial intelligence has the potential to revolutionize pest control, significantly improving mosquito monitoring and control. The application of machine learning (ML) algorithms and computer vision for the identification and classification of Aedes aegypti is a crucial part of this process. This article proposes the development of a smart trap for selective control of winged insects, combining IoT devices, high-resolution cameras, and advanced ML algorithms for insect detection and classification. The intelligent system features the YOLOv7 algorithm (You Only Look Once v7) that is capable of detecting and counting insects in real time, combined with LoRa/LoRaWan connectivity and IoT system intelligence. This adaptive approach is effective in combating Aedes aegypti mosquitoes in real time.

Great advances in automated identification systems, or ‘smart traps’, that differentiate insect species have been made in recent years, yet demonstrations of field-ready devices under free-flight conditions remain rare. Here, we describe the results of mixed-species identification of female mosquitoes using an advanced optoacoustic smart trap design under free-flying conditions. Point-of-capture classification was assessed using mixed populations of congeneric ( Aedes albopictus and Aedes aegypti ) and non-congeneric ( Ae. aegypti and Anopheles stephensi ) container-inhabiting species of medical importance. Culex quinquefasciatus , also common in container habitats, was included as a third species in all assessments. At the aggregate level, mixed collections of non-congeneric species ( Ae. aegypti , Cx. quinquefasciatus , and An. stephensi ) could be classified at accuracies exceeding 90% (% error = 3.7–7.1%). Conversely, error rates increased when analysing individual replicates (mean % error = 48.6; 95% CI 8.1–68.6) representative of daily trap captures and at the aggregate level when Ae. albopictus was released in the presence of Ae. aegypti and Cx. quinquefasciatus (% error = 7.8–31.2%). These findings highlight the many challenges yet to be overcome but also the potential operational utility of optoacoustic surveillance in low diversity settings typical of urban environments.

Background The recent rise in the transmission of mosquito-borne diseases such as dengue virus (DENV), Zika (ZIKV), chikungunya (CHIKV), Oropouche (OROV), and West Nile (WNV) is a major concern for public health managers worldwide. Emerging technologies for automated remote mosquito classification can be supplemented to improve surveillance systems and provide valuable information regarding mosquito vector catches in real time. Methods We coupled an optical sensor to the entrance of a standard mosquito suction trap (BG-Mosquitaire) to record 9151 insect flights in two Brazilian cities: Rio de Janeiro and Brasilia. The traps and sensors remained in the field for approximately 1 year. A total of 1383 mosquito flights were recorded from the target species: Aedes aegypti and Culex quinquefasciatus . Mosquito classification was based on previous models developed and trained using European populations of Aedes albopictus and Culex pipiens . Results The VECTRACK sensor was able to discriminate the target mosquitoes ( Aedes and Culex genera) from non-target insects with an accuracy of 99.8%. Considering only mosquito vectors, the classification between Aedes and Culex achieved an accuracy of 93.7%. The sex classification worked better for Cx. quinquefasciatus (accuracy: 95%; specificity: 95.3%) than for Ae. aegypti (accuracy: 92.1%; specificity: 88.4%). Conclusions The data reported herein show high accuracy, sensitivity, specificity and precision of an automated optical sensor in classifying target mosquito species, genus and sex. Similar results were obtained in two different Brazilian cities, suggesting high reliability of our findings. Surprisingly, the model developed for European populations of Ae. albopictus worked well for Brazilian Ae. aegypti populations, and the model developed and trained for Cx. pipiens was able to classify Brazilian Cx. quinquefasciatus populations. Our findings suggest this optical sensor can be integrated into mosquito surveillance methods and generate accurate automatic real-time monitoring of medically relevant mosquito species. Graphical Abstract

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO2). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO2 sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments.

Commercially available ‘smart’ trap technology has not yet been widely used to evaluate interventions against mosquitoes despite potential benefits. These benefits include the ability to capture data continuously at fine temporal scales without the human resources usually required for conventional trap deployment. Here, we used a commercially available smart trap (BG-Counter, Biogents) to assess the efficacy of an insecticide barrier treatment (BiFlex AquaMax) in reducing mosquito nuisance in a logistically challenging coastal environment in Queensland, Australia. Adoption of smart trap technology permitted us to conduct a uniquely detailed assessment of barrier treatments, ultimately allowing us to demonstrate significant reductions in mosquito collections from treated properties over all temporal scales. On average, daily mosquito collections from treated properties were reduced by 74.6% for the duration of the post-treatment period (56 d). This observation was supported by similar reductions (73.3%) in mosquito collections across all hours of the day. It was further found that underlying mosquito population dynamics were comparable across all study sites as evidenced by the high congruence in daily collection patterns among traps (Pearson r = 0.64). Despite limitations related to trap costs and replication, the results demonstrate that smart traps offer new precision tools for the assessment of barrier treatments and other mosquito control interventions.

Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF ( Ceratitis capitata , Bactrocera dorsalis and B. zonata ). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species ( C. capitata and B. zonata ). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.

As of today, pest insects such as thrips and whiteflies cause the loss of 20% - 40% of the global agricultural yield. To reduce chemical pesticide use while maintaining high-quality horticultural standards, early detection of pest infestations is essential. Although AI-assisted pest monitoring systems using sticky trap images exist today, none currently enable effective species-level detection of thrips and/or whiteflies. However, early species-level identification would allow for more targeted, species-specific control strategies, leading to reduced, localized, and more efficient pesticide application. Therefore, in this study, we evaluated the potential and limitations of real-time species-level detection of thrips ( Frankliniella occidentalis and Echinothrips americanus ) and whiteflies ( Bemisia tabaci and Trialeurodes vaporariorum ) using non-microscopic, RGB yellow sticky trap images and recent YOLO-based deep learning detection models. To this end, a balanced and labelled image dataset was gathered, consisting of the studied pest species, caught on one type of yellow sticky trap. Subsequently, various versions of the YOLO11 and YOLO-NAS detection model architectures were trained and tested using this dataset at various (digitally reduced) pixel resolutions. All tested high-resolution dataset (pixel size: 5 µm) models achieved species-level detection of the studied pests on an independent test dataset (mAP@50: 79% - 89% | F1@50: 74% - 87%). Even the smallest model (YOLO11n) delivered feasible macro-averaged (mAP@50: 80% | F1@50: 77%) and classwise performance scores (AP@50: 72% - 85% | F1@50: 68% - 82%). The minimum required pixel resolution for feasible species-level detection in greenhouse horticulture was identified as 80 µm for both the YOLO11n and YOLO11x models, enabling the use of modern smartphones, action cameras, or low-cost standalone camera modules. Combined with the low complexity and decent performance of the YOLO11n model, these results demonstrate the potential of feasible, real-time, automated species-level monitoring of (yellow) sticky traps in greenhouse horticulture. Future research should focus on extending this technology to additional pest species, sticky trap types, and ambient light conditions.

Strategic control and eradication programs for wild pigs (Sus scrofa) are being developed to help curtail the expanding populations of this invasive, alien species. Drop nets and corral traps have a long history of capturing a multitude of wildlife species, so we evaluated the effectiveness and efficiency of these traps for controlling wild pigs in southern Oklahoma. We also developed and evaluated a suspended metal trap that provided real-time monitoring and deployment to capture animals. Effectiveness of each trap type was estimated as the proportion of pigs removed from the total population, whereas efficiency was calculated based on catch per unit effort (CPUE) (i.e., the number of person hours per pig removal). During 3 years of study (2010–2012), we removed 601 pigs, 296 using drop nets, 60 using corral traps, and 245 using suspended traps. Suspended traps removed 88.1% of the estimated population, whereas drop nets removed 85.7% and corral traps removed 48.5%. CPUE was 0.64 person hours/pig using suspended traps followed by 1.9 person hours/pig for drop nets and 2.3 person hours/pig for corral traps. Drop nets and suspended traps were more effective at removing a large proportion of the population (>85%), mainly through whole sounder removal, but the suspended trap with real-time notifications was the most efficient trap type, requiring fewer person hours to operate.

Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.