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The annual global economic losses from pest-induced crop damage are substantial and difficult to quantify. Real-time monitoring of pest dynamics and timely control strategies are crucial for food security. Among the primary monitoring techniques, sex pheromone-baited trapping technology is instrumental in the detection and management of agricultural pests. To address existing limitations-such as manual insect collection and counting in conventional traps, inaccuracies in photoelectric counting devices, and the requirement for manual replacement of sticky boards in image-based traps—an advanced agricultural pest monitoring system utilizing sex pheromone bait was designed and developed in this paper. The system integrates smart electro-killing pheromone traps, a pest detection model, and a pheromone monitoring platform. Male pests attracted to pheromones are neutralized by an electric grid and deposited on an image acquisition platform. A network camera captures images of the pests, which are processed by a YOLOv9-TrapPest detection model to identify and quantify them. This model incorporates an AKConv module to enhance feature extraction, reducing false detections from limb separation. The CBAM-PANet structure improves detection rates of sticky pests, while the FocalNet module optimizes fine-grained feature capture, excluding non-target pests. The YOLOv9-TrapPest model outperforms other detection models, achieving 97.5% average precision and 98.3% mAP50 for detecting seven pest species. Furthermore, a pest pheromone monitoring platform displays the images and identification results, supporting pest control decisions. This system incorporates automated functions for pest trapping, killing, counting, and clearing, thereby achieving complete automation in the monitoring of pests attracted by sex pheromones.

Chysodeixis includens (Walker) is a polyphagous economic pest in agricultural landscapes. To detect the occurrence of this pest in the field, trapping using sex pheromone lures is often implemented. However, other plusiine species are cross-attracted to these lures and may be misidentified as C. includens due to their morphological similarities. The objectives of this study were to provide region-specific information on the abundance of C. includens throughout the year as well as document the occurrence of related plusiines cross-attracted to C. includens sex pheromone traps in the Florida Panhandle. Twelve commercial fields of peanut and twelve commercial fields of cotton located across Escambia, Santa Rosa, and Jackson counties were monitored with Trécé delta traps baited with C. includens sex pheromone lures (Alpha Scents, Inc.; West Linn, OR) from June 2017 to June 2019. There was no difference in C. includens flight across dryland or irrigated fields. Identifications revealed that in addition to C. includens, the following species of the subfamily Plusiinae were crossed-attracted: Argyrogramma verruca (Fabricius) (Lepidoptera: Noctuidae), Trichoplusia ni (Hübner) (Lepidoptera: Noctuidae), Ctenoplusia oxygramma (Geyer) (Lepidoptera: Noctuidae), and Rachiplusia ou (Guenée) (Lepidoptera: Noctuidae). The occurrence of each species in the region and their flight phenology are documented. Chysodeixis includens abundance was greatest in September and decreased through December. Due to the high abundance of C. oxygramma and similar flight phenology to C. includens, this is the likeliest species to skew estimations and influence management decisions of C. includens, especially early in the crop season, when C. includens abundance is low. Graphical Abstract

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) is attracted to its male-produced aggregation pheromone, a ∼3.5:1 mixture of (3S,6S,7R,10S)- and (3R,6S,7R,10S)-10,11-epoxy-1-bisabolen-3-ol (SSRS and RSRS respectively), and also to the pheromone of its Asian sympatric species Plautia stali Scott (Hemiptera: Pentatomidae), methyl (2E,4E,6Z)-2,4,6-decatrienoate (MDT). A stereoisomeric mixture of (7R) 10,11-epoxy-1-bisabolen-3-ols (= mixed murgantiols) is used together in commercialized products with MDT because the latter is a synergist for H. halys attraction to mixed murgantiols. However, the optimal ratio for MDT combination with mixed murgantiols, and the sensitivity of bug captures to variation in ratio of the two pheromone components, have not been tested to date. Using black pyramid traps at two sites (in Maryland and West Virginia, United States), different ratios of mixed murgantiols to MDT were tested over two entire seasons. Also, captures using various ratios of the two active pheromone stereoisomers were undertaken in month-long trials with and without MDT. Results showed that H. halys adult and nymphal captures were relatively insensitive to the ratio of synthetic pheromone (mixed murgantiols) to MDT, as long as each was present in the trap. Captures of adults and nymphs were responsive to the lure loading of the SSRS isomer, but relatively insensitive to levels of the minor component, RSRS.The relative insensitivity of H. halys to these attractant ratios gives flexibility to development of more cost-efficient synthesis and trapping as well as other semiochemical-based management tactics.

The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests.

The increasing infestation of Pink Bollworm becomes a major threat to sustainable cotton production in North cotton zone of India. The present research spanned over last six years investigates the key factors responsible for increasing infestation of pink bollworm (PBW) to a serious proportion in the North Zone of India on cotton crop. The study involves extensive field experiments and surveys to understand PBW infestation patterns and factors contributing to its establishment in a serious proportion in the zone. Studies were conducted with the objectives of monitoring PBW on Non-Bt and BG-II genotypes under unprotected conditions, pheromone trap catches, evaluating infestation patterns on BG-II hybrids, studying PBW infestation at farmer field’s locations and exploring off-season sources of carryover along with the factors supporting the initiation to serious establishments. The results revealed that prior to 2018, PBW infestation was noticed only in Non-Bt genotypes and in BG-II genotypes infestations reported from 2019 onwards and subsequently an increase in infestation was reported. Pheromone trap catches also confirmed an increasing trend in PBW infestation from 2019 onwards. Farmer’s field-level observations, from Haryana, Punjab and Rajasthan confirmed an increase in PBW damage from 2019 onwards. Among the contributing factors since 2018 to 2023, inadequate monitoring, with only 3.04% of farmers using pheromone traps as monitoring tool, 35.18% monitor PBW in rosette flower and 16.20% recognize importance of green boll destructive sampling as a tool across various locations, usage of insecticides mixtures and availability of off-season source of carryover. The study highlights the importance of implementing integrated measures for effective PBW management including timely sowing of medium to short duration genotypes, refuge planting, monitoring of Pink Bollworm infestation using various methods. Finally, the findings provide valuable information creating awareness among the growers and formulation of strategies for PBW and enhance cotton crop productivity.

The beet armyworm, (Hübner), is an important agricultural pest worldwide that has caused serious economic losses in the main crop-producing areas of China. To effectively monitor and control this pest, it is crucial to investigate its population dynamics and seasonal migration patterns in northern China. In this study, we monitored the population dynamics of using sex pheromone traps in Shenyang, Liaoning Province from 2012 to 2022, combining these data with amigration trajectory simulation approach and synoptic weather analysis. There were significant interannual and seasonal variations in the capture number of and the total number of exceeded 2,000 individuals in 2018 and 2020. The highest and lowest numbers of were trapped in September and May, accounting for 34.65% ± 6.81% and 0.11% ± 0.04% of the annual totals, respectively. The average occurrence period was 140.9 ± 9.34 days during 2012-2022. In addition, the biomass of also increased significantly during these years. The simulated seasonal migration trajectories also revealed varying source regions in different months, primarily originated from Northeast China and East China. These unique insights into the migration patterns of will contribute to a deeper understanding of its occurrence in northern China and provide a theoretical basis for regional monitoring, early warning, and the development of effective management strategies for long-range migratory pests.

The spruce bark beetle, Ips typographus , is causing severe economic losses during epidemic phases triggered by droughts and/or windstorms. Sanitation felling and salvage logging are usually the most recommended strategies to limit the damages. However, any additional control method to limit the economic impact of an outbreak would be welcome. In this respect, the efficiency of pheromone trapping is still controversial or poorly documented. In this 2-year study (2020–2021), at the peak of a severe outbreak in Belgium, we quantified the wood volume and presence/absence of new attacks at 126 sites attacked during the previous year and within 100 m from the initial attack. Each site was randomly allocated to one of three treatments: (1) three crosstraps baited with pheromones, (2) one tree-trap baited with pheromones and treated with an insecticide and (3) control sites with no trapping device. The attacked trees of the previous year were all cut and removed before the start of the experiment and newly attacked trees were removed as they were detected. The trapping devices were only active during spring to target overwintering bark beetles that might have escaped the sanitation cuts and to limit the risk of attracting dispersing beetles from outside the patch during the summer. We found a strong decrease of the attacks relative to the previous year in all treatments, including the controls (more than 50% of the control sites had no new attacks). There was no relationship between the new attacks and the attacks of the previous year. In both years, new attacks were more frequent (presence/absence) in sites with crosstraps (95% Confidence Interval [56–84%] of the sites with new attacks) than in sites with a tree-trap (26–57% - p = 0.02) and to a lesser extent than in control sites (32–63%, p = 0.08). In 2020, the attacked volumes were slightly higher in sites with crosstraps (95% Confidence Interval [3.4–14.2 m³]) than in control sites (0.2–3.5 m³, p = 0.04) and no significant difference was found with tree-trap sites (1.1–6.2 m³, p = 0.38). In 2021, there were no significant differences between the volumes attacked in the control sites (1.8–9.4 m³), crosstraps sites (0.9–6.4 m³) and tree-trap sites (0–2.5 m³). Overall, we found no evidence in favor of the efficacy of pheromone trapping during spring to reduce economic damages at the local scale when combined with sanitation felling and during a severe outbreak. The use of baited crosstraps could even be hazardous as it seemed to increase the occurrence of new attacks probably by attracting bark beetles but failing to neutralize them.

Pheromone-baited traps can be excellent tools for sensitive detection of insects of conservation concern. Here, identification of the sex pheromone of Trichopteryx polycommata (Denis & Schiffermüller, 1775), an under-recorded UK priority species, is reported. In analyses of extracts of the pheromone glands of female T. polycommata by gas chromatography coupled to electroantennographic recording from the antenna of a male moth, a single active component was detected. This was identified as (Z,Z)-6,9-nonadecadiene (Z,Z6,9-19:H) by comparison of its mass spectrum and retention times with those of the synthetic standard. In a pilot field trial in Kent, UK, T. polycommata males were caught in pheromone traps baited with lures loaded with 1 mg and 2 mg (Z,Z)-6,9-19:H. Optimum lure loading was identified in a further five trials in Kent, Sussex and Lancashire where lures of 0, 0.001, 0.01, 0.1, 1, 2, 5 and 10 mg loadings were tested. Traps baited with 1 to 10 mg of ZZ6,9-19:H caught significantly more T. polycommata than traps baited with 0 mg and 0.001 mg. In a pilot survey of T. polycommata using pheromone lures around Morecambe Bay, UK, T. polycommata males were captured at 122 new sites within the three counties where trials took place, demonstrating the potential of pheromone monitoring to increase knowledge of abundance, distribution and ecology of this elusive species.

In forest protection, various types of trap systems are used against spruce bark beetles to treat the foci of infested areas. Traditionally, these include pheromone traps, treated trap trees, and recently also the application of Storanet insecticide nets used for the sanitation of infested wood (trees), which also serve as treated trap trees when pheromones are applied, and the Trinet P system, a variation to a treated trap tree – a tripod. On an aluminium tripod, there is a Storanet insecticide net baited with a pheromone dispenser similar to other types of trap systems. The sex ratio proves to be a significant aspect in the assessment of individual types of trap systems. It is more favourable to catch a larger number of female individuals given that they bear the future development of the population. With a higher number of captured male individuals, the number of females per one male in the insect gallery increases, and the population cannot be reduced sufficiently. The results show that all three types of measures can be characterised by a lower ratio of captured males (approximately 40%) and a higher representation of females (around 60%). Differences in individual types of trap systems are insignificant.

Diverse pheromones and pheromone-based traps, as well as images acquired from insects captured by pheromone-based traps, have been studied and developed to monitor the presence and abundance of pests and to protect plants. The purpose of this study is to construct models that detect three species of pest moths in pheromone trap images using deep learning object detection methods and compare their speed and accuracy. Moth images in pheromone traps were collected for training and evaluation of deep learning detectors. Collected images were then subjected to a labeling process that defines the ground truths of target objects for their box locations and classes. Because there were a few negative objects in the dataset, non-target insects were labeled as unknown class and images of non-target insects were added to the dataset. Moreover, data augmentation methods were applied to the training process, and parameters of detectors that were pre-trained with the COCO dataset were used as initial parameter values. Seven detectors—Faster R-CNN ResNet 101, Faster R-CNN ResNet 50, Faster R-CNN Inception v.2, R-FCN ResNet 101, Retinanet ResNet 50, Retinanet Mobile v.2, and SSD Inception v.2 were trained and evaluated. Faster R-CNN ResNet 101 detector exhibited the highest accuracy (mAP as 90.25), and seven different detector types showed different accuracy and speed. Furthermore, when unexpected insects were included in the collected images, a four-class detector with an unknown class (non-target insect) showed lower detection error than a three-class detector.