Journal articles: 'Pest monitoring'

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Jeger, M. J., J. M. Waller, A. Johanson, and S. R. Gowen. "Monitoring in banana pest management." Crop Protection 15, no. 4 (June 1996): 391–97. http://dx.doi.org/10.1016/0261-2194(96)00011-7.

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FEDOR, PETER, JAROMÍR VAŇHARA, JOSEF HAVEL, IGOR MALENOVSKÝ, and IAN SPELLERBERG. "Artificial intelligence in pest insect monitoring." Systematic Entomology 34, no. 2 (January 31, 2009): 398–400. http://dx.doi.org/10.1111/j.1365-3113.2008.00461.x.

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Cardoso, Bruno, Catarina Silva, Joana Costa, and Bernardete Ribeiro. "Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network." Applied Sciences 12, no. 18 (September 19, 2022): 9397. http://dx.doi.org/10.3390/app12189397.

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With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, substantial environmental impact, and unnecessary costs both in material and manpower. Despite the potential of new technologies, pest monitoring is still done in a traditional way, leading to excessive costs, lack of precision, and excessive use of human labour. In this paper, we present an Internet of Things (IoT) network combined with intelligent Computer Vision (CV) techniques to improve pest monitoring. First, we propose to use low-cost cameras at the edge that capture images of pest traps and send them to the cloud. Second, we use deep neural models, notably R-CNN and YOLO models, to detect the Whitefly (WF) pest in yellow sticky traps. Finally, the predicted number of WF is analysed over time and results are accessible to farmers through a mobile app that allows them to visualise the pest in each specific field. The contribution is to make pest monitoring autonomous, cheaper, data-driven, and precise. Results demonstrate that, by combining IoT, CV technology, and deep models, it is possible to enhance pest monitoring.

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Pachkin, A. A., O. Yu Kremneva, R. Yu Danilov, and A. V. Ponomarev. "Vegetable Pest Monitoring Using Insect Trap Lights." Machinery and Equipment for Rural Area, no. 10 (November 8, 2021): 28–32. http://dx.doi.org/10.33267/2072-9642-2021-10-28-32.

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The results of improving the design of autonomous LED insect trap lights developed by the Federal Research Center for Plant Biological Protection are provided. The flying dynamics of the summer Helicoverpa armigera is shown. It was found that the insect trap light attracted 2.6 times more phytophagous specimens than pheromon traps. A separating element of a insect trap light been developed, which makes it possible to reduce 50 times the number of captured representatives of useful and indifferent entomofauna. The efficiency of battery charging has been increased by 43%.

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Binns, Michael R., Jan P. Nyrop, and Wopke Van Der Werf. "Monitoring Pest Abundance by Cascading Density Classification." American Entomologist 42, no. 2 (1996): 113–21. http://dx.doi.org/10.1093/ae/42.2.113.

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Berlinger, M. J., Beke Lok-Van Dijk, R. Dahan, S. Lebiush-Mordechai, and R. A. J. Taylor. "Indicator Plants for Monitoring Pest Population Growth." Annals of the Entomological Society of America 89, no. 5 (September 1, 1996): 611–22. http://dx.doi.org/10.1093/aesa/89.5.611.

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Mei, L., Z. G. Guan, H. J. Zhou, J. Lv, Z. R. Zhu, J. A. Cheng, F. J. Chen, C. Löfstedt, S. Svanberg, and G. Somesfalean. "Agricultural pest monitoring using fluorescence lidar techniques." Applied Physics B 106, no. 3 (November 12, 2011): 733–40. http://dx.doi.org/10.1007/s00340-011-4785-8.

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Wang, Mei, Xin Ju Li, Yan Yan Lu, and Shu Li Guo. "Tobacco Pest Monitoring Feasibility Analysis Based on RS." Advanced Materials Research 217-218 (March 2011): 1516–19. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1516.

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As one of the most important economic crops, tobacco is often endangered by pest disaster, which can lower the quality and output of tobacco, traditional methods can’t forecast pest disaster in time for the quantity of tobacco. This article analyzes the theory of remote sensing monitor and comes up to monitor tobacco diseases with remote sensing. It discusses the feasibility of using this technique in this article by introducing the basic theory of RS and monitoring other pest disaster.

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Ko, Alexander E. "Urban Entomology Highlights From 2020—Monitoring and Control of Urban Pests." Journal of Medical Entomology 58, no. 5 (August 3, 2021): 2012–15. http://dx.doi.org/10.1093/jme/tjab119.

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Abstract Pest management professionals aim to answer two primary questions for their customers: 1) ‘Where/What is the pest?’ and 2) ‘How do I kill it?’. These two questions drive at the core of any pest management program. 2020 was an exciting year for entomology research, with much work being done on novel technologies and methods for detecting and controlling pests. The objectives of the current publication were to discuss papers published in 2020 that addressed the key pest management objectives of 1) monitoring and 2) controlling pest populations.

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Rano, Saifatul Hossain, Mansura Afroz, and Md Mamunur Rahman. "APPLICATION OF GIS ON MONITORING AGRICULTURAL INSECT PESTS: A REVIEW." Reviews In Food and Agriculture 3, no. 1 (January 5, 2022): 19–23. http://dx.doi.org/10.26480/rfna.01.2022.19.23.

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Agricultural insect pests cause crop yield loss which results in imbalance between the food demand of world population and the global agriculture output. Pest control can be more efficient if it becomes possible to identify the current status of pest timely and take action immediately. In this regard, application of Geographic Information System (GIS) has added a new dimension for pest management. This review paper focused on the implication of GIS technology in insect pest management by facilitating decisions making process, timely planning and getting different information in many specific areas; such as monitoring suitable habitat, foliage damage index, population distribution diversity and migration of insect pests at different aspects. GIS maps, produced by using manual and remotely sensed data with the help of various hardware and software, have facilitated forecasting and early warning of insect outbreaks and their population diversity. So, that it can help researchers and farmers to take proper management planning and decisions, like when and how should they take management and control strategies against any insect pest to save agricultural fields from pest attack or outbreak. Recent advancements in the field of agriculture by using GIS technology have provided ample scope in agriculture for pest monitoring, detection and timely management with high precision and accuracy.

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Bilal, Huda. "Automated Identification and Monitoring of Fruit Fly." Current Research in Agriculture and Farming 2, no. 3 (June 30, 2021): 41–44. http://dx.doi.org/10.18782/2582-7146.144.

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The fruit fly is an invasive pest that causes significant loss in horticultural crop production by damaging the fruit quality. For the integrated management of fruit fly, monitoring is a major component. Automated pest monitoring is most promising and advanced for monitoring and accurate assessment of the status of the fruit fly. The need of the hour is to automate and monitor pest populations using advanced technologies to save time and to examine the real-time situation of the field. The purpose of this paper is to discuss the approaches and sensors which detect and monitor fruit flies automatically.The review focuses on image-based sensors for identifying fruit flies, analyzing the wing beat biometric signature with optoelectronics sensors, direct counting with photo interruption sensors and E-Traps that can provide real-time field information, and presenting the various integrated systems available.With a real-time imaging system, all information about the fruit fly population and infection rate is recorded and maintained in a database. All of this has resulted in enhanced monitoring systems for fruit fly integrated pest management.

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Clayton, Richard, and Phil Cowan. "Management of animal and plant pests in New Zealand - patterns of control and monitoring by regional agencies." Wildlife Research 37, no. 5 (2010): 360. http://dx.doi.org/10.1071/wr09072.

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Context. Significant resources are spent annually in New Zealand controlling pests to mitigate impacts on native biodiversity and agricultural production, but there are few reliable estimates of the benefits. Concerns have been expressed about inconsistent monitoring methodologies, differing frequencies and intensities of control across organisations, and poor definition of desired outcomes. Aims. To conduct and report on a survey of animal and plant pest control and monitoring by regional agencies, to identify issues with current practice and to provide advice on improvements. Methods. We surveyed 15 regional agencies in New Zealand about the pest control and associated monitoring undertaken during 2005–08. We recorded the pests targeted, the control work done and its operational details, any result and/or outcome monitoring conducted, and estimated costs. Key results. About 21% of the NZ$20 million expenditure on pest control was for monitoring. Excluding compliance (62%), monitoring changes in pest populations accounted for 31% of the total monitoring expenditure, whereas only 7% was spent measuring response in the resource that was supposedly being protected. The most common monitoring design (71%) comprised a single treatment area with no non-treatment area, in which only results were monitored. Only three programs (4%) had both treatment and non-treatment areas and both results and outcome monitoring. Conclusions. Such limited outcome monitoring constrains severely the ability of regional and local authorities to provide robust justification for their pest management activities and expenditures. Implications. Improved outcome monitoring requires better design of and additional resources for monitoring programs, improved institutional/political support for long-term programs, and better definition of long-term outcomes and objectives for pest management.

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Čirjak, Dana, Ivana Miklečić, Darija Lemić, Tomislav Kos, and Ivana Pajač Živković. "Automatic Pest Monitoring Systems in Apple Production under Changing Climatic Conditions." Horticulturae 8, no. 6 (June 14, 2022): 520. http://dx.doi.org/10.3390/horticulturae8060520.

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

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Drake, Alistair. "AUTOMATICALLY OPERATING RADARS FOR MONITORING INSECT PEST MIGRATIONS." Insect Science 9, no. 4 (December 2002): 27–39. http://dx.doi.org/10.1111/j.1744-7917.2002.tb00169.x.

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Ahmad, Mohd Najib, Abdul Rashid Mohamed Shariff, and Ramle Moslim. "Monitoring insect pest infestation via different spectroscopic techniques." Applied Spectroscopy Reviews 53, no. 10 (March 19, 2018): 836–53. http://dx.doi.org/10.1080/05704928.2018.1445094.

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Lian, Yuyang, Aqiang Wang, Sihua Peng, Jingjing Jia, Liang Zong, Xiaofeng Yang, Jinlei Li, et al. "Optimization of Sensors Data Transmission Paths for Pest Monitoring Based on Intelligent Algorithms." Biosensors 12, no. 11 (November 1, 2022): 948. http://dx.doi.org/10.3390/bios12110948.

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The harm of agricultural pests presents a remarkable effect on the quality and safety of edible farm products and the monitoring and identification of agricultural pests based on the Internet of Things (IoT) produce a large amount of data to be transmitted. To achieve efficient and real-time transmission of the sensors’ data for pest monitoring, this paper selects 235 geographic coordinates of agricultural pest monitoring points and uses genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) to optimize the data transmission paths of sensors. The three intelligent algorithms are simulated by MATLAB software. The results show that the optimized path based on PSO can make the shortest time used for transmitting data, and its corresponding minimum time is 4.868012 s. This study can provide a reference for improving the transmission efficiency of agricultural pest monitoring data, provide a guarantee for developing real-time and effective pest control strategies, and further reduce the threat of pest damage to the safety of farm products.

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Fizialetti, Giorgia, Alessandra Ventura, Mirella Fidomanzo, and Maria Grazia Filippucci. "Entomological and Thermo-Hygrometric Monitoring at the Angelica Library in Rome (Italy)." Restaurator. International Journal for the Preservation of Library and Archival Material 38, no. 2 (May 24, 2017): 153–79. http://dx.doi.org/10.1515/res-2016-0026.

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AbstractResults of entomological and thermo-hygrometric monitoring carried out at the Angelica Library in Rome are presented and discussed. Monitoring was carried out for a whole year, from April 2015 to March 2016. The purpose was to verify the possible presence of infestations of the stored goods within the building by pest species and to evaluate the possible correlation between environmental parameters found within the various areas examined and the density of the pest species found. Several pest insects were found in the library but only the beetles of the family Dermestidae can be regarded as particularly harmful and dangerous. This family was present with the genera Attagenus and Anthrenus. The presence and the frequency of other pest organisms (silverfish, booklice and isopods) can be kept under control by monitoring the environmental parameters. Psocottera were represented by the genera Liposcelis and Dorypteryx. More dangerous pest species such as anobiids, termites, cockroaches or rodents were not found in the library.

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Hausmann, Johannes. "Challenges for integrated pest management of Dasineura brassicae in oilseed rape." Arthropod-Plant Interactions 15, no. 5 (August 23, 2021): 645–56. http://dx.doi.org/10.1007/s11829-021-09861-1.

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AbstractThe use of insecticides in flowering oilseed rape (Brassica napus L.) against pest insects such as the brassica pod midge (Dasineura brassicae W.) often conflicts with the protection of pollinating and beneficial insects. Dasineura brassicae is a major pest insect in European oilseed rape production. However, a comprehensive and sustainable pest control strategy within the framework of integrated pest management (IPM) does not exist, and little research on the insect has been published during the past two decades. This paper reviews the existing knowledge about D. brassicae along its life cycle and is intended to form the basis for further research activities on pod-damaging pest insects in oilseed rape. Important knowledge gaps are identified, regarding the significance of natural enemies, diapause induction, and predictions on damage potential, based on initial pest insect population. The short lifespan of the adults is particularly challenging in praxis. The implementation of IPM for D. brassicae is discussed on the basis of the four IPM steps (set an economic threshold, establish pest monitoring, preventive measures, and direct control measures) and remaining hurdles, as well as potential solutions for a better IPM, are identified. For D. brassicae, there is no science-based economic threshold and no applicable monitoring methods for farmers, which hinders a field-specific damage forecast and the precise timing of insecticide applications. Research into improved monitoring (e.g. selective attractants, real-time monitoring using remote-sensing technologies) appears to be a promising step towards an integrated pest management of D. brassicae.

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Čirjak, Dana, Ivan Aleksi, Ivana Miklečić, Ana Marija Antolković, Rea Vrtodušić, Antonio Viduka, Darija Lemic, Tomislav Kos, and Ivana Pajač Živković. "Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks." Agriculture 13, no. 1 (December 26, 2022): 67. http://dx.doi.org/10.3390/agriculture13010067.

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The pear leaf blister moth is a significant pest in apple orchards. It causes damage to apple leaves by forming circular mines. Its control depends on monitoring two events: the flight of the first generation and the development of mines up to 2 mm in size. Therefore, the aim of this study was to develop two models using artificial neural networks (ANNs) and two monitoring devices with cameras for the early detection of L. malifoliella (Pest Monitoring Device) and its mines on apple leaves (Vegetation Monitoring Device). To train the ANNs, 400 photos were collected and processed. There were 4700 annotations of L. malifoliella and 1880 annotations of mines. The results were processed using a confusion matrix. The accuracy of the model for the Pest Monitoring Device (camera in trap) was more than 98%, while the accuracy of the model for the Vegetation Monitoring Device (camera for damage) was more than 94%, all other parameters of the model were also satisfactory. The use of this comprehensive system allows reliable monitoring of pests and their damage in real-time, leading to targeted pest control, reduction in pesticide residues, and a lower ecological footprint. Furthermore, it could be adopted for monitoring other Lepidopteran pests in crop production.

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Mckinlay, R. G. "Insect Pest Control on Potatoes." Outlook on Agriculture 17, no. 1 (March 1988): 30–34. http://dx.doi.org/10.1177/003072708801700106.

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The international importance of the potato crop needs no stressing and its protection from insect pests is correspondingly important. This article reviews the wide variety of control methods currently in use. In present circumstances integrated pest control methods are desirable on environmental grounds, but in practice the careful monitoring required makes this difficult for the individual grower.

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Dong, Shifeng, Jianming Du, Lin Jiao, Fenmei Wang, Kang Liu, Yue Teng, and Rujing Wang. "Automatic Crop Pest Detection Oriented Multiscale Feature Fusion Approach." Insects 13, no. 6 (June 18, 2022): 554. http://dx.doi.org/10.3390/insects13060554.

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Specialized pest control for agriculture is a high-priority agricultural issue. There are multiple categories of tiny pests, which pose significant challenges to monitoring. Previous work mainly relied on manual monitoring of pests, which was labor-intensive and time-consuming. Recently, deep-learning-based pest detection methods have achieved remarkable improvements and can be used for automatic pest monitoring. However, there are two main obstacles in the task of pest detection. (1) Small pests often go undetected because much information is lost during the network training process. (2) The highly similar physical appearances of some categories of pests make it difficult to distinguish the specific categories for networks. To alleviate the above problems, we proposed the multi-category pest detection network (MCPD-net), which includes a multiscale feature pyramid network (MFPN) and a novel adaptive feature region proposal network (AFRPN). MFPN can fuse the pest information in multiscale features, which significantly improves detection accuracy. AFRPN solves the problem of anchor and feature misalignment during RPN iterating, especially for small pest objects. In extensive experiments on the multi-category pests dataset 2021 (MPD2021), the proposed method achieved 67.3% mean average precision (mAP) and 89.3% average recall (AR), outperforming other deep learning-based models.

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Prasetio, Bambang, Teguh Akbar Sitorus, Jalali Ikhromi, Fachri Ambiya, Syafiq Al-Ayuby, Taufik Hidayat, and Dimas Mahendra. "PEST MONITORING ON PLANTS LONG BEANS (VIGNA UNGUICULATA L)." Journal of Agriculture 1, no. 01 (March 28, 2022): 14–16. http://dx.doi.org/10.47709/joa.v1i01.1436.

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Peanut long has Mark commercial tall and have a very big role in fulfilling need food nutrition society, especially to vegetable protein requirements. Plant this could grow and produce with good want characteristic physical loose soil, depth soil enough in To increase productivity farmer vegetable peanuts long do control pest with depending on spraying insecticide synthetic because considered more effective and efficient in its use. The aim of this research is for knowing type pests found in gardens long beans in the area Plantation Village of the District of Young Development North Batu Labuan. Result observation could be used as a base in taking control action pests by farmers. The observation was done on tana man peanut in the village of plantations in the district of Membang Muda Labuhan Batu Utara from April until June 2021. Map observed sample of 1x1 m. Observation conducted with use monitoring method. Observation data is noted every week. The result from this observation obtained that pests on plants long beans observed in the Plantation Village of Membang Muda District Labuhan Batu Utara is an aphid Aphids croccivora, and caterpillars borer Maruca testicular. Average attack both 0.83 and 5. Population pest most in the area there is caterpillar borer Maruca testicular. Pest Control Measures could be conducted with spraying chemicals or technical culture. Pest monitoring urgent conducted as base taking decision technical control.

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Ball, John, and Paul Marsan. "Establishing Monitoring Routines and Action Thresholds for a Landscape IPM Service." Arboriculture & Urban Forestry 17, no. 4 (April 1, 1991): 88–93. http://dx.doi.org/10.48044/jauf.1991.025.

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Integrated pest management (IPM) has become an important part of pest management services. However, many IPM programs are in name only, since they lack monitoring routines and action thresholds for even their most common pests. This paper discusses basic concepts of monitoring and suggests guidelines for establishing action thresholds.

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Casey, Garrett R., and Cliff I. Stains. "A fluorescent probe for monitoring PTP-PEST enzymatic activity." Analyst 145, no. 20 (2020): 6713–18. http://dx.doi.org/10.1039/d0an00993h.

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Raupp, Michael. "Monitoring: An Essential Factor to Managing Pests of Landscape Trees and Shrubs." Arboriculture & Urban Forestry 11, no. 12 (December 1, 1985): 349–55. http://dx.doi.org/10.48044/jauf.1985.074.

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Monitoring is a critical component of pest management programs for trees and shrubs. The regular inspection of plant materials for insect, disease, and cultural problems allows the pest manager to pinpoint control actions. Trapping devices can detect the presence of pests in a location and provide information for the optimal timing of control actions. Environmental monitoring can be used to fine tune controls to local climatic regimes. Incorporating several monitoring techniques into management programs enables landscape plant managers to reduce unnecessary pesticide use. This approach helps create more cost-efficient pest control programs and satisfy the growing desire of clients to reduce the risk of pesticide exposure.

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Petrovskaya, Natalia B., and Sergei V. Petrovskii. "The coarse-grid problem in ecological monitoring." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 466, no. 2122 (April 29, 2010): 2933–53. http://dx.doi.org/10.1098/rspa.2010.0023.

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Obtaining information about pest-insect population size is an important problem of pest monitoring and control. Usually, this problem has to be solved based on scarce spatial data about the population density. The problem of monitoring can thus be linked to a more general mathematical problem of numerical integration on a coarse grid. Numerical integration on coarse grids has rarely been considered in literature as it is usually assumed that the grid can be refined. However, this is not the case in ecological monitoring where fine grids are not available. In this paper, we introduce a method of numerical integration that allows one to accurately evaluate an integral on a coarse grid. The method is tested on several functions with different properties to show its effectiveness. We then use the method to obtain an estimate of the population size for different population distributions and show that an ecologically reasonable accuracy can be achieved on a very coarse grid consisting of just a few points. Finally, we summarize our mathematical findings as a protocol of ecological monitoring, thus sending a clear and practically important message to ecologists and pest-control specialists.

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Headrick, David. "The Future of Organic Insect Pest Management: Be a Better Entomologist or Pay for Someone Who Is." Insects 12, no. 2 (February 7, 2021): 140. http://dx.doi.org/10.3390/insects12020140.

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Insect pest management in certified organic production systems presents considerable challenges for growers. The Federal National Organic Program (NOP) guidelines list acceptable tactics, but their effective use requires a considerable knowledgebase in entomology. The range of tactics allowed by the NOP are viewed as limiting by many growers and there are important elements missing from the list such as pest monitoring and identification. Educational programs must consider utilizing instructional methods and additional means of outreach that introduce new pest management tactics that are individualized, regionally appropriate and emphasize grower adoption and collaboration with local professionals. This review describes the challenges and knowledge burden associated with the listed NOP pest management guidelines, provides an educational model that includes an additional level of professional support for enhanced adoption of novel pest management tactics, or refinement of current practices, with a special emphasis on the importance of insect pest population monitoring.

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Cowell, B., D. T. Johnson, R. Mizell, and M. E. Garcia. "MONITORING INSECT AND PEST DAMAGE IN PECAN IN ARKANSAS." Acta Horticulturae, no. 1070 (February 2015): 151–57. http://dx.doi.org/10.17660/actahortic.2015.1070.17.

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van der Werf, W., J. P. Nyrop, M. R. Binns, and J. Kovach. "COMPUTER-METHODOLOGY FOR DESIGNING PEST SAMPLING AND MONITORING PROGRAMS." Acta Horticulturae, no. 499 (October 1999): 175–86. http://dx.doi.org/10.17660/actahortic.1999.499.20.

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Peña, J. E. "INTEGRATED PEST MANAGEMENT AND MONITORING TECHNIQUES FOR MANGO PESTS." Acta Horticulturae, no. 645 (February 2004): 151–61. http://dx.doi.org/10.17660/actahortic.2004.645.11.

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Suto, Jozsef. "A Novel Plug-in Board for Remote Insect Monitoring." Agriculture 12, no. 11 (November 11, 2022): 1897. http://dx.doi.org/10.3390/agriculture12111897.

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The conventional approach to monitoring insect swarming is based on traps that are periodically checked by human operators. However, human checking of trap contents is expensive, and in many environments, the pest species most frequently encountered in the traps can be detected and monitored automatically. To achieve this goal, a dedicated data acquisition device is necessary, which makes real-time and online pest monitoring possible from a distant location. In addition, it is beneficial for the device to run machine learning algorithms that count and identify insects automatically from pictures. Thanks to the advantages of integrated circuits, more systems have been designed to improve integrated pest management in the context of precision agriculture. However, in our opinion, all of those systems have one or more disadvantages, such as high cost, low power autonomy, low picture quality, a WIFI coverage requirement, intensive human control, and poor software support. Therefore, the aim of this work is to present a novel plug-in board for automatic pest detection and counting. The plug-in board is dedicated to Raspberry Pi devices, especially the Raspberry Pi Zero. The proposed board, in combination with a Raspberry Pi device and a Pi camera, overcomes the limitations of other prototypes found in the literature. In this paper, a detailed description can be found about the schematic and characteristics of the board with a deep-learning-based insect-counting method.

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Kadoić-Balaško, Martina, Darija Lemić, Renata Bažok, and Ivana Pajač-Živković. "Codling moth research in Croatia." Biljni lekar 50, no. 6 (2022): 553–62. http://dx.doi.org/10.5937/biljlek2206553k.

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Codling moth is the most important pest in apple production worldwide. The pest has developed resistance to several chemical groups of insecticides, which makes its control much more difficult. Therefore, special attention is being paid to environmentally friendly protective measures, and research is also being conducted on innovative methods to monitor this important pest. In this review, a cross-section of research on codling moth in Croatia was made. The results of research on alternative control methods are presented, as well as methods for monitoring and early detection of resistant populations. Regular monitoring of resistant populations is necessary for early detection of resistant populations and allows timely application of resistance control strategies and successful control of this important pest.

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Norin, Torbjörn. "Semiochemicals for insect pest management." Pure and Applied Chemistry 79, no. 12 (January 1, 2007): 2129–36. http://dx.doi.org/10.1351/pac200779122129.

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Methods for crop protection based on semiochemicals show advantages over methods based on conventional insecticides. Applications of semiochemicals for insect pest management have, however, been limited. Some recent studies carried out in an interdisciplinary research program by five research groups in Sweden are presented. In spite of the chemodiversity in nature, it is striking that many simple and common compounds are important as chemical signals. This paper focuses on some examples of such simple signals, which are now used for monitoring and suppression of pest insects.

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Delahaut, Karen A., and Charles F. Koval. "Nursery Integrated Pest Management in Wisconsin." HortTechnology 4, no. 1 (January 1994): 72–75. http://dx.doi.org/10.21273/horttech.4.1.72.

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A Nursery Integrated Pest Management program was initiated in Wisconsin in 1991. From 1991 to 1993, the educational and monitoring program enhanced grower familiarity with the IPM concept, as well as provided detailed information on the pest problems common to woody landscape plants in Wisconsin. Educational features of the program include twilight seminars and winter workshops, a pest control guide that described the management strategies available for pests of woody landscape plants, and also statewide pest reporting and pest predictions.

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Gautam, Elina, Arvind Srivastava, Lalan Kumar Singh, Shanta Karki, Debraj Adhikari, Umesh Acharya, and Resham Bahadur Thapa. "Survey and Monitoring of Chinese Citrus Fly (Bactrocera minax Enderlein) in Sweet Orange Orchards of Sindhuli, Nepal." Nepalese Horticulture 14, no. 1 (August 26, 2020): 56–62. http://dx.doi.org/10.3126/nh.v14i1.30161.

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Chinese citrus fly, Bactrocera minax (Enderlein), is one of the most important pests of citrus. The pest is more problematic in the eastern part of the country, Nepal. Because of the difficulties associated with the control of this pest by chemical insecticides, farmers had experienced great losses in Sweet Orange. Therefore, a participatory field survey was conducted under farmer field conditions to assess losses and measure the efficacy of different local and recommended management options to address the problem of this pest. Study consisted of two major parts: monitoring of pest population and farmer’s survey. For monitoring three orchards were selected located at different altitude i.e. 1200 masl, 1300 masl and 1400 masl. Great Fruit fly Bait (25% protein hydrolysate and 0.1% abamectin) in McPhail traps were used for monitoring. Monitoring was done in every 7 days interval and lures were changed in every 15 days for the effectiveness. Only 18.3% farmers were using protein bait for the management of fruit fly. B. minax had peak population intensity at May and was found decreasing after June so the management practices should be adopted before June to prevent the loss by fruit fly. However, for best effective control attention for monitoring and management procedures has to take place throughout the life cycle of the insect.

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Reddiex, Ben, and David M. Forsyth. "Control of pest mammals for biodiversity protection in Australia. II. Reliability of knowledge." Wildlife Research 33, no. 8 (2006): 711. http://dx.doi.org/10.1071/wr05103.

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Foxes, wild dogs, feral cats, rabbits, feral pigs, and feral goats are believed to have deleterious impacts on native biodiversity in Australia. However, although considerable resources have been expended controlling these six pest species, there are few reliable estimates of the effects of pest control on native biodiversity. We first show why reliable knowledge of the effects of pest animal control operations can be gained only by adopting proper experimental designs (i.e. treatment and non-treatment areas, replication, and random assignment of treatment and non-treatment areas) and monitoring of both the pest and biodiversity. We then review the design of 1915 pest control actions conducted with the aim of protecting native biodiversity in Australia during 1990–2003. Most (67.5%) pest control actions consisted of a single treatment area without monitoring of either the pest or biodiversity. Only 2.4% of pest control actions had one or more treatment and non-treatment areas, and very few treatment and non-treatment areas (0.3%) were randomly assigned. Replication of treatment and non-treatment areas occurred in only 1.0% of pest control actions. The field of wildlife management has been strongly criticised for its slow adoption of the tenets of experimentation to examine the effects of management actions, and our results show that this criticism applies to mammalian pest control in Australia. Until the principles of experimental design are adopted, knowledge of the effects of mammalian pest control in Australia will remain unreliable.

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Wilson, Carolyn. "A review of the monitoring and management of Spotted-Wing Drosophila (Drosophila suzukii) in lowbush blueberrie." Proceedings of the Nova Scotian Institute of Science (NSIS) 49, no. 1 (March 30, 2017): 145. http://dx.doi.org/10.15273/pnsis.v49i1.6984.

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The recent arrival of spotted-wing drosophilia (Drosophila suzukii) to eastern Canada is a major threat to the million dollar lowbush blueberry industry. The highly fecund female fly lays her eggs in ripe soft-skinned fruit. The maturing larvae consume the fruit interior, ultimately resulting in fruit collapse and decreased yield. Effective monitoring and management of this pest is essential for reducing the risk of export market closure and economic losses. In this literature review, an integrated pest management plan is developed that outlines current monitoring and management practices for the pest and considers preventative physical, chemical and biological controls.

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Varga, L. "Hercinothrips femoralis (Reuter, 1891) – a new pest thrips (Thysanoptera: Panchaetothripinae) in Slovakia." Plant Protection Science 44, No. 3 (November 4, 2008): 114–18. http://dx.doi.org/10.17221/13/2008-pps.

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Monitoring of pest species in plant production and extended trade on ornamental plant material, including precise identification, are the main goals of modern plant protection. As a result, a new species of phytophagous thrips – <I>Hercinothrips femoralis</I> (Reuter, 1891) was recorded in Slovakia (experimental greenhouse in Comenius University, Bratislava) in mid 2007. Some plant specimens infested by this pest showed signs of lesions especially on young leaves. The species has been reported in more European countries recently. Due to the potential damage it can cause, increased attention should be paid to pest monitoring.

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SHARMA, RAKHEE, AMRENDER KUMAR, R. K. TANWAR, and NIRANJAN SINGH. "Web-enabled data extraction system for cotton pests." Indian Journal of Agricultural Sciences 88, no. 6 (June 14, 2018): 841–44. http://dx.doi.org/10.56093/ijas.v88i6.80574.

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Under Online Pest Monitoring and Advisory Services (OPMAS) program, huge information/data on cotton pest along with weather were collected in three intensive cotton growing zones, viz. the North Zone (Punjab, Haryana and Rajasthan), the Central Zone (Maharashtra, Madhya Pradesh and Gujarat), and the Southern Zone (Andhra Pradesh, Telangana, Karnataka and Tamil Nadu), in India. Based on pest monitoring weekly advisory services were issued to extension agencies and farmers for control measures of pests in the cotton crop. Under the project extraction system was developed which was based on three tier architecture, i.e. presentation, application and data tier to reduce the effort for searching a huge set of data for desired information on real time points. In the system, the central value of pest (mean, maximum and minimum) and spread of the pest in terms of variance and standard deviation may be obtained. These results can provide the epidemic status of the pest based on the threshold values which can be utilized to issue advisories to farmers about the pest control. In future the data extracted from this system can be used for pattern development using pest population as a character under study and time variable as an independent/explanatory variable.

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Suto, Jozsef. "Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review." Agriculture 12, no. 10 (October 19, 2022): 1721. http://dx.doi.org/10.3390/agriculture12101721.

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The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the hardware side, more camera-equipped embedded systems have been designed to remotely capture and upload pest trap images. From the software side, with the aid of machine vision and machine learning methods, traditional (manual) identification and counting can be solved by algorithm. With the appropriate combination of the hardware and software components, spraying can be accurately scheduled, and the crop-defending cost will be significantly reduced. Although automatic traps have been developed for more pest species and there are a large number of papers which investigate insect detection, a limited number of articles focus on the C. pomonella. The aim of this paper is to review the state of the art of C. pomonella monitoring with camera-equipped traps. The paper presents the advantages and disadvantages of automated traps’ hardware and software components and examines their practical applicability.

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Shortall, Chris R., Sarah A. M. Perryman, Kirstie Halsey, and Jon S. West. "The Potential of Fluorescence Imaging to Distinguish Insect Pest and Non-pest Species." Outlooks on Pest Management 33, no. 1 (February 1, 2022): 13–16. http://dx.doi.org/10.1564/v33_feb_05.

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Studying the presence and movement of insects is important in biological research for practical purposes regarding control of pests and environmental monitoring. Detection of insects by conventional trapping (e.g. the Rothamsted Insect Survey) and tracking technologies (e.g. the Rothamsted Radar Entomology Unit) have been effective for monitoring and forecasting pest migration but often require significant investment in capital costs and/or staff time. Insect detection using imaging of natural fluorescence (without additional fluorescent dyes) has been considered less, and much of the work on natural fluorescence in the animal world has been on marine organisms. Work on terrestrial arthropods has been more limited and restricted primarily to non-insect arthropods. Very early work on the distribution of fluorescent pigments in butterflies was demonstrated using long wave mercury vapour lamps followed by more work in the 1950s on butterflies, arthropods; including beetles, spiders and millipedes, cockroaches and eggs of Orthoptera. These studies often involved qualitative approaches; dissecting the animals and investigating internal organs and fluids for fluorescence as well as whole body studies. More recent studies have included quantitative work on butterflies and pest insects plus fluorescence studies in damselflies, moths, millipedes, bees and spiders. Fluorescence in juvenile stages is a property used for detection of flies and beetles in food stuffs. The vast majority of insects, however, have not been investigated for fluorescence and even in those taxa that have been studied, e.g. butterflies, the dataset is incomplete. The easiest way to observe fluorescence is to illuminate a subject with a known waveband of light in otherwise darkness and view or record an image via a filter that blocks the wavelength of the illuminating light. Any light viewed or captured at a different wavelength to the illumination, must have been produced by fluorescence. In contrast, some living organisms themselves can produce light or luminescence by internal chemical means. This work aimed to look at the potential of using natural fluorescence to detect and identify insects, particularly pests.

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Da Silva, Jéssica Maus, Ervandil Corrêa Costa, Eli Nunes Marques, and Emanuel Arnoni Costa. "Monitoring of Population of Scolytinae in Olive Culture." Journal of Agricultural Science 10, no. 8 (July 10, 2018): 457. http://dx.doi.org/10.5539/jas.v10n8p457.

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Monitoring of insects is important to analyze the species present in culture of economic interest, to verify the period of highest incidence and quantify the damages caused by the pest insects. Here, we aim to assess the diversity of bark beetles in an olive orchard. The experiment was assessed in fortnightly series throughout a year and conducted with a completely randomized experimental design, consisting of three treatments: T1: homogeneous olive culture; T2: heterogeneous olive culture; T3: border area. An ethanol trap was used for the sampling of bark beetles. The species Hypothenemus eruditus, Xyleborus linearicolis and Hypothenemus seriatus showed greater abundance among the treatments. The decrease of abundance, diversity and dominance of bark beetles occurred in the heterogeneous culture (T2). Homogeneous cultures (T1) are more susceptible to the attack of pest-insects due to the lower ecological balance among associate, predator or parasitoid insects. Border areas (T3) showed an increased diversity of arboreal species, favoring the higher incidence of insects. In spring, there is a greater incidence of bark beetles, what reinforces the need for monitoring during this period. So far, there are no evidences of bark beetles infesting olive trees in the area, or the presence of Phloeotribus scarabaeoides, which is the pest bark beetle of olive cultures.

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Aronstein, K., P. Ode, and R. H. Ffrenchconstant. "Direct Comparison of PCR-Based Monitoring for Cyclodiene Resistance in Drosophila Populations with Insecticide Bioassay." Pesticide Biochemistry and Physiology 48, no. 3 (March 1994): 229–33. http://dx.doi.org/10.1006/pest.1994.1024.

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Khromushkina, L. "Phytosanitary monitoring of warehouses." Interdepartmental Thematic Scientific Collection of Plant Protection and Quarantine, no. 63 (November 20, 2017): 194–202. http://dx.doi.org/10.36495/1606-9773.2017.63.194-202.

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The quarantine pest (Caryedon gonagra Fabr.) in a dead state was detected in the consignments of kernels peanuts imported to the of Ukraine (Kirovograd region) from India. The phytosanitary state of warehouses of the Central of Ukraine has been established. As the result of the monitoring of regulated (quarantine) pests was not detected. Species composition of stored pests on territory of the Kirovograd region was specified. There were determined 38 species of pests relating to 15 families from 3 orders.

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Rosado, Luís, Pedro Faria, João Gonçalves, Eduardo Silva, Ana Vasconcelos, Cristiana Braga, João Oliveira, et al. "EyesOnTraps: AI-Powered Mobile-Based Solution for Pest Monitoring in Viticulture." Sustainability 14, no. 15 (August 8, 2022): 9729. http://dx.doi.org/10.3390/su14159729.

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Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and intrinsic difficulties in performing pest monitoring. Conventional pest monitoring programs consist of deploying sticky traps on vineyards, which attract key insects and allow human operators to identify and count them manually. However, this is a time-consuming process that usually requires in-depth taxonomic knowledge. This scenario motivated the development of EyesOnTraps, a novel AI-powered mobile solution for pest monitoring in viticulture. The methodology behind the development of the proposed system merges multidisciplinary research efforts by specialists from different fields, including informatics, electronics, machine learning, computer vision, human-centered design, agronomy and viticulture. This research work resulted in a decision support tool that allows winegrowers and taxonomy specialists to: (i) ensure the adequacy and quality of mobile-acquired sticky trap images; (ii) provide automated detection and counting of key insects; (iii) register local temperature near traps; and (iv) improve and anticipate treatment recommendations for the detected pests. By merging mobile computing and AI, we believe that broader technology acceptance for pest management in viticulture can be achieved via solutions that work on regular sticky traps and avoid the need for proprietary instrumented traps.

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Dreistadt, Steve, and Mary Flint. "Landscape Pest Monitoring Methods and Training Managers to Use Them." Arboriculture & Urban Forestry 21, no. 1 (January 1, 1995): 1–6. http://dx.doi.org/10.48044/jauf.1995.001.

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Monitoring is the systematic collection and recording of information on pests and damage. Scale insects and aphids, respectively, can be efficiently monitored using sticky tape traps and water sensitive paper. We describe these monitoring techniques, present examples of their use in controlling citricola scale infesting Chinese hackberry and painted maple aphid infesting silver maple, and discuss our methods of training landscape pest managers to monitor.

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Patel, Shweta, Chandra Pal Singh, and Sunil Kumar Yadav. "Monitoring of insect-pest complex on rapeseed-mustard at Pantnagar." Journal of Entomological Research 43, no. 1 (2019): 73. http://dx.doi.org/10.5958/0974-4576.2019.00014.8.

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Fatzinger, Carl W., H. David Muse, Thomas Miller, and Helen Bhattacharyya. "Survey and Pest Monitoring System for Southern Pine Seed Orchards." Southern Journal of Applied Forestry 14, no. 3 (August 1, 1990): 147–54. http://dx.doi.org/10.1093/sjaf/14.3.147.

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Abstract A survey and pest monitoring system (SPMS) for pine seed orchards is described that estimates total orchard yields of female strobili and seeds, quantifies pest damage, documents the times of year when losses occur, and produces life tables for female strobili. The method utilizes sample clusters of female strobili located in the southeast crown quadrants of sample trees for obtaining three estimates per year of numbers and conditions of the two crops of cones present on seed orchard trees; additional periodic subsamples are used to determine causes of strobilus damage during periods of major loss. Interactive, user-friendly computer programs facilitate data analyses. Estimates of orchard productivity are accompanied by statistics on the precision of sampling and on the losses caused by specific pests and other factors. South. J. Appl. For. 14(3):147-159.

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Francis Fleurat-Lessard. "Monitoring insect pest populations in grain storage: the European context." Stewart Postharvest Review 7, no. 3 (2011): 1–8. http://dx.doi.org/10.2212/spr.2011.3.4.

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Blasius, Bernd. "Make your trappings count: The mathematics of pest insect monitoring." Physics of Life Reviews 11, no. 3 (September 2014): 531–32. http://dx.doi.org/10.1016/j.plrev.2014.05.005.

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Journal articles on the topic 'Pest monitoring'

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