Search Results (71)

Drosophila suzukii is an invasive pest of many fruit crops worldwide. Employing the Sterile Insect Technique (SIT) could mitigate D. suzukii population growth and crop damage. This study evaluated the efficacy of SIT on commercial fruit, by (1) validating the quality of irradiated sterile males (male mating competitiveness, courtship, and flight performance) in the laboratory, and (2) assessing population suppression and fruit damage reduction in commercial raspberry fields. Treatment with SIT was compared to the grower’s standard chemical insecticide program throughout the season. The principal metrics of efficacy were trap counts of wild adult female D. suzukii in crops and larvae per fruit during harvesting. These metrics together with monitoring of border areas allowed targeting of high-pressure areas with higher releases of sterile males, to maximise efficacy for a given release number. The sterile male D. suzukii were as competitive as their fertile non-irradiated counterparts in laboratory mating competitiveness and flight performance studies while fertility egg-to-pupae recovery was reduced by 99%. In commercial raspberry crops, season-long releases of sterile males significantly suppressed the wild D. suzukii population, compared to the grower standard control strategy; with up to 89% reduction in wild female D. suzukii and 80% decrease in numbers of larvae per harvested fruit. Additionally, relative fruit waste (i.e., percentage of harvested fruits rejected for sale) at harvest was reduced for early, mid and late harvest crops, by up to 58% compared to the grower standard control. SIT has the potential to provide an effective and sustainable strategy for managing D. suzukii in raspberries, increasing marketable yield by reducing adult populations, fruit damage and waste fruit. SIT could therefore serve as a valuable tool for integrated pest management practices in berry production systems. Full article

We studied the characteristics of adult flat grain beetles Cryptolestes pusillus Schönherr in trap counts and their relationship with insect densities using electronic probe traps in experimental bins, which contained approximately 1.1 tons of paddy (Changlixiang) with moisture contents of 10.7% and 14.0% and insect densities of 0.1, 1.0, and 5.0 adults/kg. Inside each bin, we vertically installed three layers of electronic probe traps. We installed five traps at the center and half-radius of each layer of the bin. We undertook measurements of daily trap counts, grain temperature, and intergranular relative humidity at each trap location for up to 10 days, and 1.0 kg of paddy was collected from each trap location. At each of the introduced insect densities, we detected beetles using electronic probe traps. When insect density was 0.1 adults/kg, we could not detect the existence of insects in 1.0 kg samples. It was found that the trap counts were spatially nonuniformly distributed, and there was a weak correlation between different locations. There was a regularity in the temporal distribution of trap counts, which was significantly influenced by the paddy moisture content. Except for the insect density, the moisture content, grain temperature, and intergranular relative humidity at the trap location significantly affected trap counts. The distribution pattern of beetles in paddy bulks was found and aggregated by analyzing trap counts. There was a correlation between trap counts and insect densities in grain bulks, but this correlation varied significantly across different locations. Full article

This study investigates arthropod assemblage in cowpea, hemp, and watermelon grown both as monocrops and intercrops using three sampling techniques: direct visual counts, sticky cards, and pan traps. A total of 31,774 arthropods were collected, spanning two classes [Arachnida (0.07%) and Insecta (99.93%)], 11 orders, and 82 families representing diverse functional groups. Arachnids were represented by a single family (Araneae). Among insects, the composition included Diptera (36.81%), Thysanoptera (24.64%), Hemiptera (19.43%), Hymenoptera (11.58%), Coleoptera (6.84%), Lepidoptera (0.076%) and Blattodea, Odonata, Orthoptera, Psocodea (≤0.005%). Roughly 10% of the total arthropods were pollinators, while the remainder were primarily herbivores and predators. Apidae were abundant in all treatments except for watermelon monocrops. Intercropping supported more pollinators, particularly Apidae, Halictidae, and Sarcophagidae. However, herbivores dominated (>50%) in each system, largely due to high presence of thrips and cicadellids. Predators accounted for approximately 30%, with dolichopodids (Diptera) being the most dominant. Watermelon yield increased by 30–60% in the intercrop systems. While intercropping increases overall arthropod abundance, it also creates a more balanced community where beneficial organisms are not heavily outnumbered by pests and contributes to enhanced ecological resilience and crop performance. Full article

Essential oils have emerged as promising alternatives for pest insect control. However, sublethal effects on insect reproduction and development are rarely explored, despite their relevance to integrated pest management (IPM). This study evaluated the sublethal effects of Piper hispidivervum C. DC. essential oil (EOPH) on the development and population growth of four populations of Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae), as well as the persistence of safrole residue in treated corn grains. Population development rates were determined using emergence curves and total emerged adults, while population growth was assessed by counting live insects in the feeding substrate at different storage intervals. Safrole residue persistence was analyzed using solid-phase microextraction in headspace mode (SPME-HS). Sublethal exposure to EOPH significantly reduced the development rate, total emergence, and growth in three of the four populations. The population from Crixás, GO, showed no significant reduction, with a population curve overlapping the control. The lethal dose was reduced by 98.20%, indicating low persistence and potential food safety. The EOPH exhibited sublethal effects on S. zeamais populations, reducing both development rates and population growth. This reduction varied among the populations studied. Further research is encouraged to explore its effects on different insect populations and under broader environmental conditions. Full article

Rice (Oryza sativa L.) has long been threatened by the brown planthopper (BPH, Nilaparvata lugens) and white-backed planthopper (WBPH, Sogatella furcifera). It is difficult to detect and count rice planthoppers from RGB images, and there are a limited number of publicly available datasets for agricultural pests. This study publishes a publicly available planthopper dataset, explores the potential of YOLOv8-p2 and proposes an efficient improvement strategy, designated SwinT YOLOv8-p2, for detecting and counting BPH and WBPH from RGB images. The Swin Transformer was incorporated into the YOLOv8-p2 in the strategy. Additionally, the Spatial and Channel Reconstruction Convolution (SCConv) was applied, replacing Convolution (Conv) in the C2f module of YOLOv8. The dataset contains diverse pest small targets, and it is easily available to the public. YOLOv8-p2 can accurately detect different pests, with mAP50, mAP50:95, F1-score, Recall, Precision and FPS up to 0.847, 0.835, 0.899, 0.985, 0.826 and 16.69, respectively. The performance of rice planthopper detection was significantly improved by SwinT YOLOv8-p2, with increases in mAP50 and mAP50:95 ranging from 1.9% to 61.8%. Furthermore, the correlation relationship between the manually counted and detected insects was strong for SwinT YOLOv8-p2, with an R² above 0.85, and RMSE and MAE below 0.64 and 0.11. Our results suggest that SwinT YOLOv8-p2 can efficiently detect and count rice planthoppers. Full article

The fall armyworm, Spodoptera frugiperda, is a serious invasive pest of the family Noctuidae (Lepidoptera) that poses a significant threat to global crop production, with poaceae crops being particularly affected. Previous studies have indicated that, as a voracious insect, the fall armyworm possesses the potential for food source diversification. However, to date, limited research has been conducted on whether plants other than maize (Zea mays L.) and rice (Oryza sativa L.) can serve as potential food resources for the pest. In Yunnan Province, China, the distribution ranges of the fall armyworm and Ficus plants show a significant degree of overlap. Ficus species, including the widely distributed Ficus microcarpa L. f., commonly grow within or near cornfields. Our previous field studies have documented instances of fall armyworms in cornfields exhibiting feeding behavior on F. microcarpa. In this study, maize and F. microcarpa were selected as food resources for fall armyworms to compare larval feeding preferences, development time, survival rate, and reproductive capacity. The results demonstrated that when both maize and F. microcarpa were available simultaneously, fall armyworm larvae consumed both plant species. Further analysis revealed that larvae feeding on F. microcarpa exhibited a significantly longer developmental period from the third stage to pupation (14.08 ± 0.44 d) compared to those feeding on maize (9.21 ± 0.14 d). Moreover, the pupae size, pupae weight, and egg count were reduced by approximately 10%, 30%, and 30%, respectively, in larvae that fed on F. microcarpa. Despite these physiological challenges, our research findings indicated that, despite F. microcarpa not being the primary food source for fall armyworms under natural conditions, fall armyworms feeding on F. microcarpa were still capable of completing the life cycle from the third instar to the second generation when relying solely on F. microcarpa. Therefore, it is crucial to strengthen the observation and monitoring of fall armyworm populations feeding on F. microcarpa and implement targeted control strategies according to specific circumstances, thereby preventing F. microcarpa from acting as a potential host. Full article

The greenbug aphid (Schizaphis graminum (Rondani)) is a major pest of wheat and an important vector of wheat viruses. An RNA-seq study was conducted to investigate the microbial effects of two greenbug genotypes, the presence or absence of cereal yellow dwarf virus, and the condition of the wheat host over a 20-day time course of unrestricted greenbug feeding. Messenger RNA reads were mapped to ca. 47,000 bacterial, 1218 archaeal, 14,165 viral, 571 fungal, and 94 protozoan reference or representative genomes, plus greenbug itself and its wheat host. Taxon counts were analyzed with QIIME2 and DESeq2. Distinct early (days 1 through 10) and late (days 15 and 20) communities differed in the abundance of typical enteric genera (Shigella, Escherichia, Citrobacter), which declined in the late community, while the ratio of microbial to greenbug read counts declined 50% and diversity measures increased. The nearly universal aphid endosymbiont, Buchnera aphidicola, accounted for less than 25% of the read counts in both communities. There were 302 differentially expressed (populated) genera with respect to early and late dates, while 25 genera differed between the greenbug genotypes and nine differed between carrier and virus-free greenbugs. The late community was likely responding to starvation as the wheat host succumbed to aphid feeding. Our results add to basic knowledge about aphid microbiomes and offer an attractive alternative method to assess insect microbiomes. Full article

Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and computational memory. Therefore, this study presents a YOLO-SGInsects model based on YOLOv8s for tiny stored-grain insect detection on the surface of grain bulk by adding a tiny object detection layer (TODL), adjusting the neck network with an asymptotic feature pyramid network (AFPN), and incorporating a hybrid attention transformer (HAT) module into the backbone network. The YOLO-SGInsects model was trained and tested using a GrainInsects dataset with images captured from granaries and laboratory. Experiments on the test set of the GrainInsects dataset showed that the YOLO-SGInsects achieved a stored-grain insect pest detection mean average precision (mAP) of 94.2%, with a counting root mean squared error (RMSE) of 0.7913, representing 2.0% and 0.3067 improvement over the YOLOv8s, respectively. Compared to other mainstream approaches, the YOLO-SGInsects model achieves better detection and counting performance and is capable of effectively handling tiny stored-grain insect pest detection in grain bulk surfaces. This study provides a technical basis for detecting and counting common stored-grain insect pests on the surface of grain bulk. Full article

Insecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP₅₀). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments. Full article

Insect pests strongly affect crop growth and value globally. Fast and precise pest detection and counting are crucial measures in the management and mitigation of pest infestations. In this area, deep learning technologies have come to represent the method with the most potential. However, for small-sized crop pests, recent deep-learning-based detection attempts have not accomplished accurate recognition and detection due to the challenges posed by feature extraction and positive and negative sample selection. Therefore, to overcome these limitations, we first designed a co-ordinate-attention-based feature pyramid network, termed CAFPN, to extract the salient visual features that distinguish small insects from each other. Subsequently, in the network training stage, a dynamic sample selection strategy using positive and negative weight functions, which considers both high classification scores and precise localization, was introduced. Finally, several experiments were conducted on our constructed large-scale crop pest datasets, the AgriPest 21 dataset and the IP102 dateset, achieving accuracy scores of 77.2% and 29.8% for mAP (mean average precision), demonstrating promising detection results when compared to other detectors. Full article

Insects play essential roles in ecosystems, providing services such as pollination and pest regulation. However, global insect populations are in decline due to factors like habitat loss and climate change, raising concerns about ecosystem stability. Traditional insect monitoring methods are limited in scope, but advancements in AI and machine learning enable automated, non-invasive monitoring with camera traps. In this study, we leverage the new Diopsis dataset that contains images from field operations to explore an approach that emphasizes both background extraction from images and the SAHI approach. By creating augmented backgrounds from extracting insects from training images and using these backgrounds as canvases to artificially relocate insects, we can improve detection accuracy, reaching mAP50 72.7% with YOLO10nano, and reduce variability when counting insects on different backgrounds and image sizes, supporting efficient insect monitoring on low-power devices such as Raspberry Pi Zero W 2. Full article

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. Full article

Accelerating oxygen depletion during hermetic storage can minimize pest damage and preserve product quality. This study evaluated the effectiveness of hand warmers in accelerating hypoxia to control insect pests inside hermetic containers. We used one, two, or four hand warmers to deplete oxygen in a 4-gallon hermetic jar with 4 kg of cowpea and cowpea bruchids, alongside a non-hermetic control with cowpea bruchids and no hand warmers. Oxygen levels, insect mortality, egg counts, seed moisture content, and germination rates were monitored over 2, 5, or 8 days of storage. Only the four hand warmers treatment reduced oxygen levels below 1% within 12 h and maintained them for up to 168 h. The other treatments did not achieve this level. Insect mortality was higher with more hand warmers and extended storage duration, reaching 100% after 5 and 8 days with four and two hand warmers, respectively. Similarly, increased hand warmers and extended storage durations reduced egg counts and adult emergence. The treatments did not affect the moisture content or germination rates of the stored cowpea seeds. Hand warmers proved effective in accelerating hypoxia during hermetic storage, resulting in high insect mortality and reduced reproduction, without compromising grain quality. Full article

In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system using the European Pepper Moth, Duponchelia fovealis (Lepidoptera: Crambidae), as a study model. A prototype water trap equipped with an infrared digital camera controlled using a microprocessor served as the attraction and capture device. Images captured by the system in the laboratory were processed to detect objects. Subsequently, these objects were labeled, and size and shape features were extracted. A machine learning model was then trained to identify the number of insects present in the trap. The model achieved 99% accuracy in identifying target insects during validation with 30% of the data. Finally, the prototype with the trained model was deployed in the field for result confirmation. Full article

Understanding the spatial patterns of insect pests and their associations with their environments is crucial for developing effective sampling and management plans. This study was conducted to identify optimal sampling units for the hemlock woolly adelgid, Adelges tsugae Annand (Hemiptera: Adelgidae) and to characterize its spatial distribution patterns in hemlock (Tsuga canadensis (L.) Carrière) stands in West Virginia, USA. To determine the optimal sampling unit, we randomly selected 24 branches from each of 46 A. tsugae-infested hemlock trees. The locations and number of A. tsugae ovisacs on each branch were recorded and the coefficient of variation was used to choose the optimal sampling units. To determine the spatial patterns of A. tsugae, each of the three 1-ha hemlock stands was divided into 100 grids, and ovisac counts were taken from the center of each grid. Semivariograms and spatial analysis by distance indices (SADIE) were used to analyze the spatial patterns of A. tsugae. In addition, various environmental and biological factors were measured to explore their spatial associations with A. tsugae. The results of this study revealed that the A. tsugae ovisacs exhibited spatial aggregation within branches, predominantly at the tips, and a 50 cm branch approximately 3 m above the ground was the optimal sampling unit. The spatial aggregation of A. tsugae in the hemlock stands was evident, and positive spatial associations were found between A. tsugae populations and factors including the aspect, tree diameter at breast height, and tree height. These findings offer valuable insights for the sampling and management of A. tsugae. Full article