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Abstract: Field studies of biodegradable polymerized vegetable oil-coated paper mulches were conducted to determine if these could replace non-degradable polyethylene mulches for stopping weeds and promoting growth of cottonwood trees. Tests were conducted over two growing seasons in two adjacent field sites in southeastern Missouri. At the end of the 2001 season, eastern cottonwood trees grown on the coated paper mulches had average heights (4.57–4.66m, 15.0–15.3ft), which were not significantly different from the control black polyethylene mulch (4.75m, 15.6ft). Tree heights were significantly less for uncoated paper mulch (4.45m, 14.6ft) or no mulch (3.90m, 12.8ft), presumably due to heavy weed growth around the trees. Uncoated paper mulch was extensively degraded after only about 4 weeks, while the coated paper persisted until the fall. Addition of ZnO to the oil coating delayed the onset of visible degradation, such as the formation of holes and tears, especially near the buried edge and above the drip tube. Similar results were seen for the 2002 study except that weed growth was not extensive so that tree heights for the mulched and bare plots were not significantly different. These results suggest that polymerized vegetable oil-coated paper mulches can function as effective mulches during the first year of tree growth, and thus, eliminate the need to use non-degradable polyethylene mulches. [Copyright &y& Elsevier]

Investigation to manage rodent pests in wheat crop sown with different tillage methods was conducted. The results revealed maximum efficacy in the form of control success (%) on plain bait consumption and burrow count basis with conventional (92.30) followed by zero tillage (84.74) and maize residue tillage (74.38). In these, rodenticide treatments with double burrow baiting was followed by single paper baiting. Lesser % cut tillers (0.39-0.53) and damage 9.56-15.71 kg/ 0.4 ha were observed in these. Burrow count in maize residue and zero tillage was 2.15 and 1.60x more than conventional tillage. Thus, rodenticide treatments with double burrow baiting at vegetative stage in December (bromadialone followed by zinc phosphide at 15 days interval) along with paper baiting (bromadialone) at reproductive stage (end February to 1st week of March) can be recommended against rodents in wheat crop to get higher economic returns (Rs.452.36-556.81/ 0.4 ha). [ABSTRACT FROM AUTHOR]

Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production. [ABSTRACT FROM AUTHOR]

Natural enemies of agricultural pests, such as parasitoids and predators, often use chemical and visual cues in search of their hosts and prey, and they can learn the association between the cues and the host and prey presence. The braconid, egg-larval endoparasitoid wasp Ascogaster reticulata is a promising biological control agent for tortricid pests, such as Adoxophyes honmai, in tea plantations. Although previous studies revealed that A. reticulata uses contact chemicals released by tea plants in response to tortricid egg oviposition and that it can learn the associated cues, the diurnal wasp is also expected to use visual cues, especially color. Therefore, in this study, we investigated the innate color preference and associative color learning ability of A. reticulata. When a green paper and a paper of a different color (black, blue, red or yellow) was offered together to naive females of the wasp, the females spent less time on a black and blue papers. However, wasps trained to associate black or blue with the presence of a host egg-mass showed increased preference for these colors, whereas red- and yellow-trained wasps did not show changes in preference. Our findings indicate that A. reticulata uses colors, in addition to chemical cues, in host searching behavior and has the ability to learn colors associated with host presence. [ABSTRACT FROM AUTHOR]

This paper proposes PestNet, a lightweight method for classifying crop pests, which improves upon MobileNet-V2 to address the high model complexity and low classification accuracy commonly found in pest classification research. Firstly, the training phase employs the AdamW optimizer and mixup data augmentation techniques to enhance the model's convergence and generalization capabilities. Secondly, the Adaptive Spatial Group-Wise Enhanced (ASGE) attention mechanism is introduced and integrated into the inverted residual blocks of the MobileNet-V2 model, boosting the model's ability to extract both local and global pest information. Additionally, a dual-branch feature fusion module is developed using convolutional kernels of varying sizes to enhance classification performance for pests of different scales under real-world conditions. Lastly, the model's activation function and overall architecture are optimized to reduce complexity. Experimental results on a proprietary pest dataset show that PestNet achieves classification accuracy and an F1 score of 87.62% and 86.90%, respectively, marking improvements of 4.20 percentage points and 5.86 percentage points over the baseline model. Moreover, PestNet's parameter count and floating-point operations are reduced by 14.10% and 37.50%, respectively, compared to the baseline model. When compared with ResNet-50, MobileNet V3-Large, and EfficientNet-B1, PestNet offers superior parameter efficiency and floating-point operation requirements, as well as improved pest classification accuracy. [ABSTRACT FROM AUTHOR]

Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model's ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model's generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection. [ABSTRACT FROM AUTHOR]

Simple Summary: The red palm weevil is an exotic and highly endangered pest that is extremely harmful to palm plants. In order to effectively control this pest, we propose an algorithm to automatically detect and count adult red palm weevils in traps. Previously, the trapping and counting of adult red palm weevils was done manually. The population density and damage level were then inferred from the number of adults trapped to guide control efforts. However, the efficiency of this method is very low. The algorithm proposed in this paper solves the drawbacks of manual counting, and the recognition accuracy reaches 93.8%, which also improves the efficiency of agricultural monitoring. The red palm weevil (RPW, Rhynchophorus ferrugineus) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs' management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests. [ABSTRACT FROM AUTHOR]

Food security is a basic human right that guarantees humans an adequate amount of nutritious food. However, plant viruses and agricultural pests cause real damage to food sources, leading to negative impacts on meeting the human right of obtaining a sufficient amount of food. Understanding infectious disease dynamics can help us to design appropriate control and prevention strategies. Although cassava is among the most produced and consumed crops and greatly contributes to food security, cassava mosaic disease causes a decrease in photosynthesis and reduces cassava yield, resulting in a lack of crops. This paper developed a fractional model for cassava mosaic disease (CMD) dynamics based on the Caputo–Fabrizio (CF) fractional derivative to decrease cassava plant infection. We used fixed-point theory to study the existence of a unique solution in the form of the CMD model. A stability analysis of the model was conducted by using fixed-point theory and the Picard technique. A new numerical scheme was proposed for solving the nonlinear system of a fractional model in the sense of the CF-derivative and applied to obtain numerical simulations for a fractional model of the dynamics of CMD. The obtained results are described using figures that show the dynamics and behaviors of the compartments of CMD, and it is concluded that decreasing the population of whitefly vectors can prevent cassava plants from becoming infected better than increasing the recovery rate of the infected cassava plants. [ABSTRACT FROM AUTHOR]

Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers' personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM). [ABSTRACT FROM AUTHOR]

Agriculture has a crucial impact on the economic, ecological, and social development of the world. More rapid and precise prevention and control work, especially for accurate classification and detection, is required due to the increasing severity of agricultural pests and diseases. However, the results of the image classification and detection are unsatisfactory because of the limitation of image data volume acquisition and the wide range of influencing factors of pests and diseases. In order to solve these problems, the vision transformer (ViT) model is improved, and a multi-scale and multi-factor ViT attention model (SFA-ViT) is proposed in this paper. Data augmentation considering multiple influencing factors is implemented in SFA-ViT to mitigate the impact of insufficient experimental data. Meanwhile, SFA-ViT optimizes the ViT model from a multi-scale perspective, and encourages the model to understand more features, from fine-grained to coarse-grained, during the classification task. Further, the detection model based on the self-attention mechanism of the multi-scale ViT is constructed to achieve the accurate localization of the pest and disease. Finally, experimental validation of the model, based on the IP102 and Plant Village dataset, is carried out. The results indicate that the various components of SFA-ViT effectively enhance the final classification and detection outcomes, and our model outperforms the current models significantly. [ABSTRACT FROM AUTHOR]

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area. [ABSTRACT FROM AUTHOR]

Pyrethroids are used in agricultural to control pests on a variety of crops. Bifenthrin, a synthetic pyrethroid, is a broad spectrum insecticide. It acts mainly on the nervous system of vertebrates as well as invertebrates. It is susceptible to the biodegradation by some soil bacteria. The present paper deals with the toxicological studies of bifenthrin and its metabolites benzene 1,1(methylthio) ethylidine, resorcinol and monochloro trifluromethane that were produced by a newly isolated strain of Paracoccus siganidrum APGM1 on earthworms, which play an important role in the improvement of the soil fertility. The toxicity was assessed by 48 hrs filter paper contact test, 14 days soil test and histopathological methods. The results of filter paper contact test revealed that the earthworms were more susceptible to bifenthrin than their metabolites. The LC50 value of bifenthrin and its metabolites was 6 ppm and 20 ppm respectively. The soil test showed that at 6 ppm concentration of bifenthrin, half the number of earthworms died after 14 days and with the increase in the concentration, mortality increased. At 10 ppm concentration, all the earthworms died. However, half the number of earthworms died after 14 days at the metabolite concentration of 20 ppm. The adverse effects of bifenthrin and very little effect of their metabolites on the morphological properties and structural integrity of the tissues were observed in histopathological studies. Thus, the metabolites of bifenthrin were less toxic to earthworms than the bifenthrin. [ABSTRACT FROM AUTHOR]

Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention. [ABSTRACT FROM AUTHOR]

This paper describes a novel qualitative dataset regarding coffee pests based on the ancestral knowledge of coffee farmers in the Department of Cauca, Colombia. The dataset has been obtained from a survey applied to coffee growers with 432 records and 41 variables collected weekly from September 2020 to August 2021. The qualitative dataset includes climatic conditions, productive activities, external conditions, and coffee bio-aggressors. This dataset allows researchers to find patterns for coffee crop protection through the ancestral knowledge not detected by real-time agricultural sensors. As far as we are concerned, there are no datasets like the one presented in this paper with similar characteristics of qualitative value that express the empirical knowledge of coffee farmers used to detect triggers of causal behaviors of pests and diseases in coffee crops. Dataset:  https://doi.org/10.5281/zenodo.8275090. Dataset License: Licensed under Creative Commons Attribution 4.0 International (CC-BY-4.0). [ABSTRACT FROM AUTHOR]

Simple Summary: Reducing the use of pesticides that harm natural enemies of crop pests is important to pest management. Currently, there is limited information on how herbicides might affect natural enemies. We carried out an analysis of existing data to determine how herbicides affect natural enemies and which herbicides are the most harmful. Herbicides increased natural enemy mortality and reduced their longevity and efficacy as predators. Predatory mites and predatory true bugs were more susceptible to herbicides than other groups of natural enemies. Spiders and parasitoid wasps were the least susceptible. However, parasitoid wasps are often tested in their "protected" juvenile stages within their hosts, and the data may under-represent the sensitivity of adults. Some potential glyphosate replacement herbicides were more harmful than glyphosate. There was little or no data available for many herbicides and beneficial insects, indicating that much more research is needed on this topic. A critical component of integrated pest management is minimizing disruption of biological control by reducing the use of pesticides with significant non-target effects on natural enemies. Insecticide non-target effects testing for natural enemies has become increasingly common, but research examining the non-target effects of herbicides on natural enemies is scarce, and recommendations regarding herbicide selectivity are non-existent. We used meta-analysis to summarize laboratory bioassays testing non-target effects of herbicides on arthropod natural enemies and identify patterns in taxon susceptibility and active ingredient toxicity. Data were extracted from 78 papers representing 801 total observations. Herbicides increased natural enemy mortality and decreased longevity, reproduction, and predation. Mesostigmatan mites and hemipterans were the most sensitive to herbicides, and spiders, neuropterans, and hymenopterans were the least sensitive. Mortality was higher in juvenile predators versus parasitoids but did not differ between adults; parasitoid juveniles are likely better protected within the host. In terms of acute mortality, metribuzin, glufosinate, and oxyfluorfen were the most harmful herbicides. Only nicosulfuron, rimsulfuron, pendimethalin, phenmedipham, atrazine, and urea did not increase natural enemy mortality. The large effect size of glufosinate is particularly concerning, as it is the most likely replacement herbicide for glyphosate in many crops. Many active ingredients remain under-studied. Our analysis indicates that herbicides have a strong potential to disrupt biological control in cropping systems. [ABSTRACT FROM AUTHOR]

The use of Internet of Things (IoT) technology for real-time monitoring of agricultural pests is an unavoidable trend in the future of intelligent agriculture. This paper aims to address the difficulties in deploying models at the edge of the pest monitoring visual system and the low recognition accuracy. In order to achieve that, a lightweight GCSS-YOLOv5s algorithm is proposed. Firstly, we introduce the lightweight network GhostNet, use the Ghostconv module to replace the traditional convolution, and construct the C3Ghost module based on the CSP structure, drastically reducing the number of model parameters. Secondly, during the feature fusion process, we introduce the content-aware reassembly of features (CARAFE) lightweight up-sampling operator to enhance the feature integration capability of the pests by reducing the impact of redundant features after fusion. Then, we adopt SIoU as the bounding box regression loss function, which enhances the convergence speed and detection accuracy of the model. Finally, the traditional non-maximum suppression (NMS) was improved to Soft-NMS to improve the model's ability to recognize overlapping pests. According to the experimental results, the mean average precision (mAP) of the GCSS-YOLOv5s model reaches 90.5%. This is achieved with a 44% reduction in the number of parameters and a 7.4 G reduction in computation volume compared to the original model. The method significantly reduces the model's resource requirements while maintaining accuracy, which offers a specific theoretical foundation and technological reference for the future field of intelligent monitoring. [ABSTRACT FROM AUTHOR]

Simple Summary: Invasive alien species, such as Carpomya vesuviana Costa (Diptera: Tephritidae), threaten global biodiversity, agriculture, and public health. This study used advanced modelling techniques to predict the potential distribution of C. vesuviana and understand its environmental impacts. The findings suggest that climate change is driving the geographic expansion of C. vesuviana, primarily in Asia, Africa, and Australia. The species is projected to shift poleward by the 2090s, as the species will have shifted to the polar regions under the influence of climate change. Effective management strategies are crucial to mitigate the environmental and health impacts of its expansion. Invasive alien species (IAS) significantly affect global native biodiversity, agriculture, industry, and human health. Carpomya vesuviana Costa, 1854 (Diptera: Tephritidae), a significant global IAS, affects various date species, leading to substantial economic losses and adverse effects on human health and the environment. This study employed biomod2 ensemble models, multivariate environmental similarity surface and most dissimilar variable analyses, and ecological niche dynamics based on environmental and species data to predict the potential distribution of C. vesuviana and explore the environmental variables affecting observed patterns and impacts. Compared to native ranges, ecological niche shifts at invaded sites increased the invasion risk of C. vesuviana globally. The potential geographical distribution was primarily in Asia, Africa, and Australia, with a gradual increase in suitability with time and radiation levels. The potential geographic distribution centre of C. vesuviana is likely to shift poleward between the present and the 2090s. We also show that precipitation is a key factor influencing the likely future distribution of this species. In conclusion, climate change has facilitated the expansion of the geographic range and ecological niche of C. vesuviana, requiring effective transnational management strategies to mitigate its impacts on the natural environment and public health during the Anthropocene. This study aims to assess the potential threat of C. vesuviana to date palms globally through quantitative analytical methods. By modelling and analysing its potential geographic distribution, ecological niche, and environmental similarities, this paper predicts the pest's dispersal potential and possible transfer trends in geographic centres of mass in order to provide prevention and control strategies for the global date palm industry. [ABSTRACT FROM AUTHOR]

Research on agricultural pest control data indicates that the actual effectiveness of insecticides may exhibit slight variations, influenced by factors such as the type and quantity of pests. Additionally, the development of resistance by pests, as a result of adaptation to the environment, can impact the precision of pest control strategies. Hence we suggest implementing a nonlinear pulse state-dependent feedback control system, considering resource limitations, to investigate the influence of varying pesticide fatality rates on pest outbreaks. This paper takes into account the fear effect and anti-predator behavior to accurately portray the farmland ecosystem. We assessed the phase set and Poincaré map under the conditions for the existence of order-k (where k = 1 , 2 , 3 ) periodic solutions and examined their stability behavior. Remarkably, the system exhibited diverse bifurcation phenomena associated with pesticide-related parameters. Furthermore, the system demonstrated a multistability phenomenon involving the coexistence of the order-1 periodic solution and the limit cycle. This suggests that altering control strategies can disturb the initial coexistence status of pests and natural enemies. It also underscores that resource limitations can indeed impact the outbreak patterns and frequencies of pests. In addition to the variability in pesticide fatality rates, the inclusion of natural enemy releases in the nonlinear pulse strategy contributes to the model exhibiting complex dynamic characteristics. All these findings are substantiated by numerical simulations. [ABSTRACT FROM AUTHOR]

Insecticides have been instrumental in the prevention and management of key agricultural insect pests, thereby contributing to increased food production. However, the effectiveness of insecticides diminishes when targeted pests develop resistance, a phenomenon linked to species evolution and survival instincts. The Arthropod Pesticide Resistance Database, maintained by Michigan State University, has documented 18,934 cases of resistance to insecticidal active ingredients worldwide from 1914 to the present. This special collection comprises eight original research articles that delve into the monitoring, mechanism and management of insecticide resistance. Two papers present multi‐year resistance monitoring results in Chilo suppressalis and Spodoptera litura. Two studies employ transcriptome data to examine the candidate genes implicated in solanine‐induced response in Phthorimaea operculella and chlorantraniliprole‐induced response in Galeruca daurica, respectively. Additionally, two papers describe the genetic and physiological impacts of insecticides, while others explore the potential mechanisms underlying chlorfenapyr resistance in Glyphodes pyloalis and pyrethroids resistance in Laodelphax striatellus. The new results, conclusions and suggestions presented in this collection are anticipated to contribute to the advancement of knowledge on insecticide resistance and facilitate the development of a sustainable approach to managing insect pests. [ABSTRACT FROM AUTHOR]

The invasion of agricultural diseases and insect pests is a huge difficulty for the growth of crops. The detection of diseases and pests is a very challenging task. The diversity of diseases and pests in terms of shapes, colors, and sizes, as well as changes in the lighting environment, have a massive impact on the accuracy of the detection results. We improved the C2F module based on DenseBlock and proposed DCF to extract low-level features such as the edge texture of pests and diseases. Through the sensitivity of low-level features to the diversity of pests and diseases, the DCF module can better cope with complex detection tasks and improve the accuracy and robustness of the detection. The complex background environment of pests and diseases and different lighting conditions make the IP102 data set have strong nonlinear characteristics. The Mish activation function is selected to replace the CBS module with the CBM, which can better learn the nonlinear characteristics of the data and effectively solve the problems of gradient disappearance in the algorithm training process. Experiments show that the advanced Yolov8 algorithm has improved. Comparing with Yolov8, our algorithm improves the MAP50 index, Precision index, and Recall index by 2%, 1.3%, and 3.7%. The model in this paper has higher accuracy and versatility. [ABSTRACT FROM AUTHOR]

Deroceras reticulatum also known as the Grey Field Slug is a serious agricultural and horticultural pest. Integrated pest management of D. reticulatum and other pest molluscs includes the use of chemical mollusicides, crop management practices and biological control. Research into the biology and control of D. reticulatum requires animals for experiments; however, collection of wild animals for these purposes is often not feasible due to variations in seasonal availability or geographic distribution. Additionally, the use of wild animals introduced a large amount of variation in experiments due to differing health status, age and prior environmental exposure of the animals. This paper describes protocols for the establishment and maintenance of a colony of D. reticulatum in the laboratory which can provide large numbers of eggs or animals of different ages for study. [ABSTRACT FROM AUTHOR]

Fruit tree diseases are one of the major agricultural disasters in China. With the popularity of smartphones, there is a trend to use mobile devices to identify agricultural pests and diseases. In order to identify leaf diseases of apples more easily and efficiently, this paper proposes a cascade backbone network-based (CBNet) disease identification method to detect leaf diseases of apple trees in the field. The method first replaces traditional convolutional blocks with MobileViT-based convolutional blocks particularly for feature extraction. Compared with the traditional convolutional block, the MobileViT-based convolutional block is able to mine feature information in the image better. In order to refine the mined feature information, a feature refinement module is proposed in this paper. At the same time, this paper proposes a cascaded backbone network for effective fusion of features using a pyramidal cascaded multiplication operation. The results conducted on field datasets collected using mobile devices showed that the network proposed in this paper can achieve 96.76% accuracy and 96.71% F1-score. To the best of our knowledge, this paper is the first to introduce Transformer into apple leaf disease identification, and the results are promising. [ABSTRACT FROM AUTHOR]

Accurate and efficient real-time detection of orchard pests was essential and could improve the economic benefits of the fruit industry. The orchard pest dataset, PestImgData, was built through a series of methods such as web crawler, specimen image collection and data augmentation. PestImgData was composed of two parts, PestImgData-1 and PestImgData-2. It contained 24,796 color images and covered 7 types of orchard pests. Based on the PestImgData and YOLOv4 algorithm, this paper conducted a preliminary study on the real-time object detection of orchard pests from 4 perspectives: transfer learning, activation function, anchor box, and batch normalization. In addition, this paper also visualized the feature learning ability of the detection models. On the basis of the above research, three improvement measures were adopted: the post-processing NMS algorithm was upgraded to DIoU-NMS, the training method was upgraded to 2-time finetuning training and the training data was enhanced. The performance of the improved model, F-D-YOLOv4-PEST, had been effectively improved. The mean average precision of F-D-YOLOv4-PEST was 92.86%, and the detection time of a single picture was 12.22 ms, which could meet the real-time detection requirements. In addition, in the case of high overlap area or high density, F-D-YOLOv4-PEST still maintained good performance. In the testing process of the laboratory and the greenhouse, including the wired network and the wireless network, F-D-YOLOv4-PEST could locate and classify pests as expected. This research could provide technical reference for the intelligent identification of agricultural pests based on deep learning. [ABSTRACT FROM AUTHOR]

The research on crop pest identification technology based on image analysis has important practical significance for effectively controlling the occurrence of crop diseases and insect pests (CDIP), improving crop yield and reducing the pollution of pesticides to the environment. Aiming at the problems existing in crop pest recognition technology and aiming at improving the accuracy and efficiency of crop pest recognition, this study proposes a crop pest image recognition method based on transfer learning and convolution neural network. Firstly, the crop pest image is geometrically operated to expand the crop pest image data set, and then the expanded data set is divided into training set and test set. Then, Alexnet, VGG-16, and ResNet-50 models are pretrained on ImageNet large image data set. Based on the theory of transfer learning, the learned model parameters are transferred to the small sample image data set of crop diseases and insect pests in this paper, so as to train the crop diseases and insect pests recognition model. This method is tested on Image Database for Agricultural Diseases and Pests (IDADP). The experimental results show that the convolution neural network plant leaf image recognition method based on transfer learning has better effect. It can quickly and accurately diagnose crop diseases, reduce the use of pesticides and fertilizers, and improve the yield and quality of crops. [ABSTRACT FROM AUTHOR]

Pest detection: This process is essential for the early warning of pests in the agricultural sector. However, the challenges posed by agricultural pest datasets include but are not limited to species diversity, small individuals, high concentration, and high similarity, which greatly increase the difficulty of pest detection and control. To effectively solve these problems, this paper proposes an innovative object detection model named MACNet. MACNet is optimized based on YOLOv8s, introducing a content-based feature sampling strategy to obtain richer object feature information, and adopts distribution shifting convolution technology, which not only improves the accuracy of detection but also successfully reduces the size of the model, making it more suitable for deployment in the actual environment. Finally, our test results on the Pest24 dataset verify the good performance of MACNet; its detection accuracy reaches 43.1 A P which is 0.5 A P higher than that of YOLOv8s, and the computational effort is reduced by about 30%. This achievement not only demonstrates the efficiency of MACNet in agricultural pest detection, but also further confirms the great potential and practical value of deep learning technology in complex application scenarios. [ABSTRACT FROM AUTHOR]

Simple Summary: The reduviid genus Sycanus Amyot & Serville, 1843, possesses higher aesthetic value and plays a crucial role as a natural enemy in the control of agricultural and forestry pests. However, Sycanus from China has not undergone a comprehensive review for over four decades. Based on both molecular data and morphological evidence, we conducted a systematic review of the 14 Chinese Sycanus species, including a description of three new species. Due to the variability of body coloration and morphological similarity among closely related species, unresolved issues and debates still persist in the taxonomic study of the genus Sycanus from China. In this study, we conducted phylogenetic analyses and species delimitation for Sycanus in China based on a COI DNA barcoding dataset comprising 81 samples. The results revealed that all the samples could be classified into 12 species by integrating molecular analyses with morphological comparison. This paper provides a comprehensive systematic review of the Sycanus species found in China, including descriptions of three new species: S. taiwanensis Zhao & Cai sp. nov., S. flavicorius Li & Cai sp. nov., and S. hainanensis Wang & Cai sp. nov. Furthermore, it is proposed that S. croceovittatus Dohrn, 1859, S. leucomesus Walker, 1873, and S. villicus Stål, 1863, are three synonyms of S. bifidus (Fabricius, 1787); S. bicolor Hsiao, 1979, is a synonym of S. versicolor Dohrn, 1859; and S. hsiaoi Maldonado-Capriles, 1990, is a synonym of S. marginellus Putshkov, 1987. Additionally, brief biological information is provided for two species, S. falleni Stål, 1863, and S. croceus Hsiao, 1979. [ABSTRACT FROM AUTHOR]

More than 25 years ago, Donald Stokes argued that we must move beyond the false dichotomy of basic or applied research and suggested that when considering a program of scientific research it is important to ask whether (i) the work is motivated by use and (ii) if there is a search for fundamental understanding. Giving yes/no answers to these questions allows us to characterize research more fully, replacing the "or" of "basic or applied" by a richer understanding of the process of science. Stokes proposed that research that was motivated by a consideration of use and sought fundamental understanding be called research in Pasteur's Quadrant. One advantage of such work is that the search for fundamental understanding means that the problem-solving tools are more likely to be transferrable. After reviewing Stokes's formulation of research, I illustrate it with examples from the control of tephritid flies and the use of insect parasitoids for biological control. Thinking about one's work within Stokes's framework has many advantages for individual scientists, including guidance for journal selection, how to organize and conclude papers and seminars, and the "elevator speech." Furthermore, since research in Pasteur's Quadrant has the characteristic of simultaneously increasing our understanding of how the world works and improving applications, it will more likely benefit the community of pest scientists. [ABSTRACT FROM AUTHOR]

Simple Summary: Grasshopper and cricket (Orthoptera) mass occurrences represent an unusually large increase in populations of certain species. Grasshopper species known as locusts are well known to cause severe damage to many crops. The severity of each outbreak depends on the species and on a wide range of ecological factors, including temperature and rainfall regime. In Croatia, a small country in SE Europe, there has never been systematic research on these Orthoptera species, and this phenomenon is regarded as a rare event despite several huge outbreaks being reported in the past. For the first time, all known mass occurrences of grasshoppers, locusts, crickets, bush-crickets, and mole crickets (of the order Orthoptera) from Croatia are summarized. The study represents a historical overview of all Orthoptera mass occurrences known based on the literature but also provides many new records. It seems that Orthoptera mass occurrences are not rare in the country at all, especially in the Mediterranean part. The present situation suggests that thorough research into locusts as agricultural pests in Croatia should be conducted to determine the potential threat to the local economy and with special emphasis on the Adriatic region. The trends of climate change in the Mediterranean imply a rise in locust destructiveness risk in the near future and invites further research. During the last century, well-known locust species, such as Calliptamus italicus and Dociostaurus maroccanus, have produced outbreaks of varying degrees in the Balkans. The literature data on outbreaks in the region are scarce, and Croatia is not an exception. This study summarized the data on 23 Orthoptera mass occurrences in Croatia from 1900 to 2023 from 28 localities, representing 12 species. This is a low level of outbreak activity compared with other locust and pest grasshopper species in other parts of the world. The species with the most reporting is C. italicus with altogether six mass occurrences, while second is Barbitistes ocskayi and Miramella irena with three records, and in the third, place D. maroccanus and Gryllotalpa sp., each with two mass occurrences having been reported. One of the most notable swarms is that of Anacridium aegyptium which occurred around Šibenik in 1998, and this paper provides the first account of it, 25 years after it took place. The most recent outbreaks took place in 2022, and the most notable one was that of D. maroccanus swarm in Štikovo. The 2022 and 2023 reports were brief and muted, despite the affected agriculturists claiming significant damages. [ABSTRACT FROM AUTHOR]

The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model named YOLOv5s-pest is proposed. Firstly, we design a hybrid spatial pyramid pooling fast (HSPPF) module, which enhances the model's capability to capture multi-scale receptive field information. Secondly, we design a new convolutional block attention module (NCBAM) that highlights key features, suppresses redundant features, and improves detection precision. Thirdly, the recursive gated convolution ( g 3 C o n v ) is introduced into the neck, which extends the potential of self-attention mechanism to explore feature representation to arbitrary-order space, enhances model capacity and detection capability. Finally, we replace the non-maximum suppression (NMS) in the post-processing part with Soft-NMS, which improves the missed problem of detection in crowded and dense scenes. The experimental results show that the mAP@0.5 (mean average precision at intersection over union (IoU) threshold of 0.5) of YOLOv5s-pest achieves 92.5% and the mAP@0.5:0.95 (mean average precision from IoU 0.5 to 0.95) achieves 72.6% on the IP16. Furthermore, we also validate our proposed method on other datasets, and the outcomes indicate that YOLOv5s-pest is also effective in other detection tasks. [ABSTRACT FROM AUTHOR]

Timely and effective pest detection is essential for agricultural production, facing challenges such as complex backgrounds and a vast number of parameters. Seeking solutions has become a pressing matter. This paper, based on the YOLOv5 algorithm, developed the PestLite model. The model surpasses previous spatial pooling methods with our uniquely designed Multi-Level Spatial Pyramid Pooling (MTSPPF). Using a lightweight unit, it integrates convolution, normalization, and activation operations. It excels in capturing multi-scale features, ensuring rich extraction of key information at various scales. Notably, MTSPPF not only enhances detection accuracy but also reduces the parameter size, making it ideal for lightweight pest detection models. Additionally, we introduced the Involution and Efficient Channel Attention (ECA) attention mechanisms to enhance contextual understanding. We also replaced traditional upsampling with Content-Aware ReAssembly of FEatures (CARAFE), which enable the model to achieve higher mean average precision in detection. Testing on a pest dataset showed improved accuracy while reducing parameter size. The mAP50 increased from 87.9% to 90.7%, and the parameter count decreased from 7.03 M to 6.09 M. We further validated the PestLite model using the IP102 dataset, and on the other hand, we conducted comparisons with mainstream models. Furthermore, we visualized the detection targets. The results indicate that the PestLite model provides an effective solution for real-time target detection in agricultural pests. [ABSTRACT FROM AUTHOR]

Accurate recognition of pests can effectively reduce the negative impact of pests on the agricultural economy. The unprofessional shooting ways result in low-resolution images, which seriously influences the accuracy of pest recognition. The super-resolution reconstruction method can transform low-resolution images into high-resolution images. The existing super-resolution reconstruction networks have the problems such as feature missing, low feature utilization, and a large amount of computation. The multi-scale adaptive learning network (MALNet) proposed in this paper is based on residual and dense connections and consists of four modules: rear-compensation enlargement module (RCEM), low-frequency feature building module (LFFBM), multi-scale details representation module (MsDRM), and feature channels correction module (FCCM). First, the low-frequency contour feature is built by LFFBM, and MsDRM extracts local detail features of the pest. After that, FCCM enhances important features, being fused with the enlarged feature map generated by RCEM. The experimental results show that, compared with other reconstruction networks, MALNet achieves an average improvement of 0.26 dB and 0.006 in peak signal- to-noise ratio and structural similarity. [ABSTRACT FROM AUTHOR]

As it is well known, Romania is an important cultivator and producer of wheat in the European Union. Due to the pedo-climatic conditions that our country has, wheat is found in all development regions, and especially in the South-Muntenia Region. Here, in 2020, 602,794 ha were cultivated, representing 28% of Romania's total area cultivated with wheat. The production obtained was 1,753,248 tons, and accounted for 27% of Romania's total wheat production. Unfortunately, this crop faces diseases and pests that can affect plant nutrition and development, and ultimately influences indicators such as the average production and total production. Contamination of cereals with toxic fungal metabolites is one of the main problems of contemporary agriculture. Fusarium species are the most dangerous pathogens of cereals and have a high level of toxicity. These metabolites are the main cause of the development of cereal fusarium (FHB). Under the existing natural conditions at Albota-Pitesti Agricultural Research and Development Station (ARDS), Arges County the paper presents several studies on the mineral nutrition of winter wheat subject to the risk of contamination with Fusarium toxins. It should be noted that the types of soil on which the experiments were performed were Vertic Luvisols. Five winter wheat varieties were selected for testing: Delabrad, Dropia, Faur, Glosa and Gruia. The phenophases in which the mineral nutrition of the plants was characterized with macro and micronutrients, were those of the ear emergence-flowering. [ABSTRACT FROM AUTHOR]

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the "lack of data" and "false insect detection". By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled. [ABSTRACT FROM AUTHOR]

Simple Summary: Various types of rice pests cause huge losses to rice production every year in China. In this paper, a deep neural network for pest detection and classification via digital images is proposed. The targeted optimization is improved for the pest characteristics. Our experiments determined that our model has a higher accuracy and detection speed compared with other methods. In addition, it can be more widely used in pest detection surveys for various crops. In recent years, the occurrence of rice pests has been increasing, which has greatly affected the yield of rice in many parts of the world. The prevention and cure of rice pests is urgent. Aiming at the problems of the small appearance difference and large size change of various pests, a deep neural network named YOLO-GBS is proposed in this paper for detecting and classifying pests from digital images. Based on YOLOv5s, one more detection head is added to expand the detection scale range, the global context (GC) attention mechanism is integrated to find targets in complex backgrounds, PANet is replaced by BiFPN network to improve the feature fusion effect, and Swin Transformer is introduced to take full advantage of the self-attention mechanism of global contextual information. Results from experiments on our insect dataset containing Crambidae, Noctuidae, Ephydridae, and Delphacidae showed that the average mAP of the proposed model is up to 79.8%, which is 5.4% higher than that of YOLOv5s, and the detection effect of various complex scenes is significantly improved. In addition, the paper analyzes and discusses the generalization ability of YOLO-GBS model on a larger-scale pest data set. This research provides a more accurate and efficient intelligent detection method for rice pests and others crop pests. [ABSTRACT FROM AUTHOR]

Simple Summary: Over the past two decades, researchers around the world have conducted studies to identify volatile compounds that serve as attractants for one of the most important agricultural pests, the western flower thrips (WFT). In this review, the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used to find published studies and to extract the information generated worldwide to determine the overall attraction of the reported compounds using a meta-analysis approach. This systematic review shows that the most-used compounds worldwide are those based on pyridines, which show a high attraction ratio. Methyl isonicotinate is the most studied of these compounds. Data from the meta-analysis accurately estimated the potential attraction of other novel compounds that could be used for integrated pest management strategies, such as monitoring WFT populations or mass trapping. A call for new research evaluating and developing new products with these novel compounds is made. The study of the semiochemicals of the western flower thrips (WFT), Frankliniella occidentalis, Pergande (Thysanoptera: Thripidae), is a relevant topic that spans the last two decades. Approximately a hundred articles published on this subject from 2000 to 2022 can be found in academic databases, representing approximately 5% of the research on this important pest. These topics have generated a platform for novel research with a high potential for development. However, to move on to a new research step, an effectiveness evaluation of the compounds discovered so far is necessary. This review conducted a systematic analysis of the research focused on the semiochemicals (kairomones, pheromones, and attractants) for this pest. Papers from the past three decades on WFT attraction to semiochemicals were collected from databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The number of individuals attracted to compounds was extracted from the papers and compiled for analysis. With this information, an attraction ratio was calculated. Forty-one possible attractants were found in the literature, with methyl isonicotinate being the most-studied compound so far, with the third-highest attraction ratio. δ-Decalactone was the compound with the highest attraction ratio, but it was one of the least studied. A meta-analysis of the WFT choosing proportion was performed for the compounds with more trials found in the literature. The predicted mean choice percentages for methyl isonicotinate (MIN) and Lurem-TR, the MIN's commercial product, were 76.6% and 66.6%, respectively. There was a convergence among the analyzed studies showing a high degree of research focus on the same group of nitrogen-containing compounds (mainly the pyridine structure). These findings call for future research to diversify the discovery and evaluation of attractive compounds in this relevant study area. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model for named entity recognition of Chinese crop diseases and pests. The model is intended to solve the problems of uneven entity distribution, incomplete recognition of complex terms, and unclear entity boundaries. First, a robustly optimized BERT pre-training approach-whole word masking (RoBERTa-wwm) model is used to extract diseases and pests' text semantics, acquiring dynamic word vectors to solve the problem of incomplete word recognition. Adversarial training is then introduced to address unclear boundaries of diseases and pest entities and to improve the generalization ability of models in an effective manner. The context features are obtained by the bi-directional gated recurrent unit (BiGRU) neural network. Finally, the optimal tag sequence is obtained by conditional random fields (CRF) decoding. A focal loss function is introduced to optimize conditional random fields (CRF) and thus solve the problem of unbalanced label classification in the sequence. The experimental results show that the model's precision, recall, and F1 values on the crop diseases and pests corpus reached 89.23%, 90.90%, and 90.04%, respectively, demonstrating effectiveness at improving the accuracy of named entity recognition for Chinese crop diseases and pests. The named entity recognition model proposed in this study can provide a high-quality technical basis for downstream tasks such as crop diseases and pests knowledge graphs and question-answering systems. [ABSTRACT FROM AUTHOR]

Following the rapid development of information and communication technology, and the huge amounts of data that have undergone explosive growth, artificial intelligence and machine learning have been used for predictive analysis in many fields. However, the prediction accuracy of these machine learning recognition models depends on the quality of the features selected for training. It is therefore very important to analyse characteristics that are meaningful and in line with the target variables as the training conditions for machine learning recognition models. In this paper, we analyse the correlation between features and target variables using the Pearson product-moment correlation coefficient, and integrate transfer learning technology for sequential feature extraction to enhance the prediction accuracy of a machine learning recognition model for the prediction of multiple crop pests and diseases as the performance verification target of the proposed method. The performance of our machine learning recognition model is compared with schemes in related work, and our approach is shown to increase the prediction accuracy by between 3% and 15%. [ABSTRACT FROM AUTHOR]

Pest management has been a great challenge since the beginning of human agricultural activities. Since the 1930s, chemical pesticide control has been a major control technology that can solve some of the pest problems in agricultural production. Still, it is harmful to food safety and the ecological environment. Meanwhile, the extensive use of chemical pesticides may lead to the rapid development of pest resistance. Because of the advantages of low cost, eco-friendly advantage, and low side effects, Solar Insecticidal Lamp (SIL) as the main physical control technology has been widely used for pest management in agricultural production in China. Owing to the phototaxis of pests, they have a phototropic rhythm during the nighttime. We can adjust the SIL insecticidal time according to the phototropic rhythm of pests. The purpose of this paper is to provide a comprehensive review of the pest phototactic rhythm in a selection of 24 pest species. It is the first comprehensive survey on the phototactic rhythm of pests and the time segments of this survey are accurate to the hour. The phototactic rhythm of pests are investigated in two different varieties of crops: 1) food crops and 2) economic crops. We also discuss and analyze the various factors (e.g., meteorological conditions, insecticidal devices, physiological states and others) that affect the changing phototactic rhythm of pests. Finally, we highlight some open research challenge issues and future directions. [ABSTRACT FROM AUTHOR]

Giant African snail has been considered as the most challenging and evolving pest all over the world. This Pest is recognized as one of the topmost 100 destructive species in the world. This Snail is an agricultural pest devours only vegetables, fruits and sometimes sand. It also disturbs the households. It has the capacity to transmit both human and plant pathogens. This snail feeds on the stem, leaves, and flower of the cauliflower. This paper gives the brief study of various behavior of Giant African snail in acoustic environment. The Signals of various behavior of snails are recorded and analyzed using a MATLAB 2019 software. The time domain specification and frequency domain specification of pest Snails are done. Eradication of snail from its habitat is the tedious process. The eradication of snail is quite simple when it is detected automatically. This paper also suggests the identification of snails automatically by its acoustic signals and uses Machine Learning algorithm for effective detection. Added to above, the addition of the 5% of AgNP nanoparticles eliminated the presence of Giant African snail in the agricultural fields. [ABSTRACT FROM AUTHOR]

Tetranychus urticae (the two-spotted spider mite) is a phytophagous agricultural pest that affects many economically important crops. Two-spotted spider mites are group-living arachnids that produce silk webbing which is used for protection and dispersal, and also influences settlement behavior of conspecifics. We investigated the effects of conspecific silk and frass on microhabitat selection of these mites and whether these materials are substrates for semiochemicals. Two-choice behavioral assays gave mites the option of settling in the presence or absence of cues produced by conspecifics. Both sexes of mites preferred to settle in the presence of silk produced by conspecific females, and males also responded positively to silk produced by other males. Frass alone had no effect on mite settlement choices, but mites tended to prefer frass in combination with silk over silk alone. Female mites did not respond to conspecific silk after it was washed with methanol, and a crude methanol extract of the silk was sufficient to elicit settlement behavior, confirming that a semiochemical is responsible for the effect. We conclude that silk-bound semiochemicals provide cues used by both male and female mites when selecting a microhabitat, and that frass may also provide relevant social cues. [ABSTRACT FROM AUTHOR]

Automatic identification and location of farmland pests are an important direction of target detection research. The wide variety of pests and the similarity between pest categories make the automatic identification of farmland pests have some problems, such as high error rate and difficult identification. In order to achieve a better target for automatic identification and location of farmland pests, this paper proposes a lightweight pest detection model, and the network is the EfficientNet proposed by Google, which achieves the detection of 26 pests, the idea based on the classical Yolo target detection algorithm. First of all, features were extracted through the lightweight backbone, and then multiscale feature fusion is performed by PANet; finally, three feature matrices with different sizes were output to predict pests of different sizes. Using CIOU as the loss function of regression prediction better reflects the relative position of the prior box and the real box. The experimental results are compared with other lightweight algorithms, and the results show that the accuracy rate of the algorithm for identification and localization of agricultural pest in this paper is the highest and could reach 93.73%. Moreover, the model is lightweight and can be deployed on low-cost equipment, which reduces the cost of equipment and accurately predicts the status of pests and diseases in farmland. In practice, it is shown that the algorithm can effectively solve the problems of large number of pests, pest accumulation, background interference, and has strong robustness. [ABSTRACT FROM AUTHOR]

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It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of precision agriculture technology worldwide, plant protection unmanned aerial vehicles (UAVs) have been widely applied in the prevention and control of agricultural pests and diseases due to their advantages such as unrestricted terrain, strong maneuverability, hover capability, and high operational efficiency. In view of the fact that most of the current path planning methods for plant protection UAVs are mainly applicable to large fields with relatively flat terrain, and there is a lack of three-dimensional path planning methods applicable to hilly orchards, as well as a lack of intuitive evaluation of the advantages and disadvantages of the paths by utilizing the amount of energy consumption, this paper proposes a three-dimensional (3D) path planning method for plant protection UAVs in hilly orchards. The method utilizes the three-dimensional (3D) coordinates of the operational area obtained from a realistic 3D model of the area, and determines the 3D operational path through the use of Boustrophedon Cellular Decomposition and the height variations of the operational area. Based on the induced power used by the plant protection UAV for flight and the power needed to overcome wind resistance, combined with the change of the load of the plant protection UAV and the supply situation, the relative energy consumption model of the plant protection UAV is constructed. Then, by optimizing the operational heading angle (1–180°), the 3D path with the minimum relative energy consumption is obtained. Through a simulation test, it is demonstrated that the path with the minimum relative energy consumption (operational heading angle of 133°) reduces the average relative energy consumption by 62.44% and 47.21% compared to the path with the maximum relative energy consumption (operational heading angle of 51°) and the average relative energy consumption, respectively. This proves the feasibility of the proposed path planning method in hilly orchard environments and provides a reference for the application of plant protection UAVs in such orchards. [ABSTRACT FROM AUTHOR]

Background: The contribution of the microbiome to pesticide breakdown in agricultural pests remains unclear. We analyzed the effect of pirimiphos-methyl (PM) on four geographically different cultures of the stored product pest mite Acarus siro (6 L, 6Tu, 6Tk and 6Z) under laboratory experiments. The effect of PM on mite mortality in the impregnated filter paper test was compared. Results: The mite sensitivity to PM decreased in the order of 6 L, 6Tu, 6Tk, and 6Z. Then, the mites were cultured on PM residues (0.0125 and 1.25 µg·g−1), and population growth was compared to the control after 21 days of exposure. The comparison showed two situations: (i) increasing population growth for the most sensitive cultures (6 L and 6Tu), and (ii) no effect on mite population growth for tolerant cultures (6Z and 6Tk). The microbiome of mites was analyzed by quantification of 16S DNA copies based on quantitative polymerase chain reaction (qPCR) and by barcode sequencing of the V4 fragment of 16S DNA on samples of 30 individuals from the control and PM residues. The microbiome comprised primarily Solitalea-like organisms in all cultures, except for 6Z, followed by Bacillus, Staphylococcus, and Lactobacillus. The microbiomes of mite cultures did not change with increasing population density. The microbiome of cultures without any differences in population density showed differences in the microbiome composition. A Sodalis-like symbiont replaced Solitalea in the 1.25 µg·g−1 PM in the 6Tk culture. Sodalis and Bacillus prevailed in the microbiomes of PM-treated mites of 6Z culture, while Solitalea was almost absent. Conclusion: The results showed that the microbiome of A. siro differs in composition and in response to PM residues in the diet. The results indicate that Sodalis-like symbionts can help recover mites from pesticide-induced stress. [ABSTRACT FROM AUTHOR]

Agriculture has been considered an important source of food for humans throughout history. Plant pests cause significant damage to crops and reduce the productivity of global crop yields. Therefore, it is important to identify the plant pest at an earlier stage in order to minimize crop losses and use pesticides optimally. This paper develops the MobileENet deep learning architecture for accurate plant pest identification with less computational effort. The input images are pre‐processed, and the features are extracted using a deep convolutional encoder–decoder network (DCEDN). The proposed classification approach solves the problems of over‐fitting regularization, batch normalization, and dropout layers. Due to the minimum computing size and factorization process, the classification performance is increased. It extracts discriminatory feature information by eliminating redundant background information. The performance of the proposed approach is evaluated on the IP102 dataset, and the performance is compared with existing deep learning‐based approaches. The performance metrics, such as accuracy, precision, recall, and so forth, are considered to evaluate the performance of the proposed plant pest identification approach. The accuracy performance of the proposed approach is improved to 98.83% with less information loss. [ABSTRACT FROM AUTHOR]

This article explores the feelings English people had about, and with, pigeons in early modern England and America. In so doing, the paper uses the concept of an emotional ecology, an understanding of emotion that is situated in a web of relations with other creatures and things. To do this, the paper outlines the context of everyday interactions with pigeons in England and uses these contexts to explore how rural tenants felt about the pigeons in their midst. Hungry, fecund and gregarious, pigeons cut a divisive figure in the English landscape. Legal sanction and customary practice marked them as privileged while a wide range of other potential agricultural pests were enthusiastically suppressed. We then look at how settler colonists experienced an abundance of American pigeons, particularly the Passenger Pigeon which would be hunted to extinction in the nineteenth century. Together, this story provides an example of how environments, culture and emotions are mutually, if not equally, constitutive. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly adopted in agriculture to monitor environmental variables to predict the presence of pests. Differently from these approaches, this paper introduces aWSN to detect the presence of snails in the field. The network can be used to both trigger an alarm of early pest presence and to further elaborate statistical models with the addition of environmental data as temperature or humidity to predict snail presence. In this paper we also design our own WSN simulator to account for real-life conditions as an uneven spacing of motes in the field or different currents generated by solar cells at the motes. This allows achieving more realistic network deployment in the field. Experimental tests are included in this paper, showing that our motes are perpetual in terms of energy consumption. [ABSTRACT FROM PUBLISHER]

Mycopesticides are living preparations that use fungal cells, such as spores and hyphae, as active ingredients. They mainly include mycoinsecticides, mycofungicides, mycoherbicides and nematophagous fungi. The utilization of fungi for controlling agricultural pests can be traced back to approximately 1880, when entomopathogenic fungi were initially employed for this purpose. However, it was not until 1965 that the world's first mycopesticide, Beauveria bassiana, was registered as Boverin® in the former Soviet Union. In past decades, numerous novel mycopesticides have been developed for their lower R&D costs, as well as the environmentally friendly and safe nature. In this review, we investigated the mycopesticides situation of registration in USA, EU, China, Canada and Australia. Superisingly, it was found that the registered mycopesticides are extremely raised in recent years. Currently, the insecticides, fungicides (nematocides) and herbicides were respectively registered 27, 53 and 8 fungal strains. This paper also analyzes the main problems currently faced by mycopesticides and offers suggestions for their future development. [ABSTRACT FROM AUTHOR]