Log in

Relevant bibliographies by topics / Pest detection

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Pest detection'

Author: Grafiati

Published: 18 April 2022

Last updated: 30 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Pest detection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Pest detection"

1

Rana, Harshil, and Reema Pandya. "Pest Detection System." International Journal of Computer Sciences and Engineering 9, no. 12 (2021): 23–25. http://dx.doi.org/10.26438/ijcse/v9i12.2325.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Guo, Boyu, Jianji Wang, Minghui Guo, Miao Chen, Yanan Chen, and Yisheng Miao. "Overview of Pest Detection and Recognition Algorithms." Electronics 13, no. 15 (2024): 3008. http://dx.doi.org/10.3390/electronics13153008.

Full text

Abstract:

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest det

APA, Harvard, Vancouver, ISO, and other styles

3

Zhu, Ruixue, Fengqi Hao, and Dexin Ma. "Research on Polygon Pest-Infected Leaf Region Detection Based on YOLOv8." Agriculture 13, no. 12 (2023): 2253. http://dx.doi.org/10.3390/agriculture13122253.

Full text

Abstract:

Object detection in deep learning provides a viable solution for detecting crop-pest-infected regions. However, existing rectangle-based object detection methods are insufficient to accurately detect the shape of pest-infected regions. In addition, the method based on instance segmentation has a weak ability to detect the pest-infected regions at the edge of the leaves, resulting in unsatisfactory detection results. To solve these problems, we constructed a new polygon annotation dataset called PolyCorn, designed specifically for detecting corn leaf pest-infected regions. This was made to addr

APA, Harvard, Vancouver, ISO, and other styles

4

P. Radha, V. Arockia Mary Epsy,. "Pest Detection Using Image Denoising and Cascaded Unet Segmentation for Pest Images." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 1359–71. http://dx.doi.org/10.52783/tjjpt.v44.i4.1040.

Full text

Abstract:

This study proposes a novel approach for pest detection in pest images using image denoising and cascaded UNET segmentation. The proposed approach involves the use of a hybrid neural network RESNET50 with CNN for image dataset training, optimized using Believed Adam Optimization. The images are then preprocessed using a superior MLP model for image denoising, which enhances the image quality and reduces the noise present in the image. The images are then passed through an adaptive UNET architecture for image segmentation, which is based on domain adaptation and semantic segmentation. The casca

APA, Harvard, Vancouver, ISO, and other styles

5

Doan, Thanh-Nghi. "Large-Scale Insect Detection With Fine-Tuning YOLOX." International Journal of Membrane Science and Technology 10, no. 2 (2023): 892–915. http://dx.doi.org/10.15379/ijmst.v10i2.1306.

Full text

Abstract:

With the aim of detecting insect pests at an early stage, there has been an increasing demand for insect pest detection and classification, particularly in large-scale setups. Therefore, the aim of this research is to introduce a new real-time pest detection technique using a deep convolutional neural network, which not only offers improved accuracy but also faster speed and less computational effort. The networks were constructed using various modern object detector models such as YOLOv4, YOLOv5, and YOLOX. Our proposed networks were evaluated on a standard large-scale insect pest dataset, IP

APA, Harvard, Vancouver, ISO, and other styles

6

Fang, Hao, Binbin Shi, Yongpeng Sun, Neal Xiong, and Lijuan Zhang. "APest-YOLO: A Multi-Scale Agricultural Pest Detection Model Based on Deep Learning." Applied Engineering in Agriculture 40, no. 5 (2024): 553–64. http://dx.doi.org/10.13031/aea.15987.

Full text

Abstract:

HighlightsWe propose a APest-YOLO model, an innovative agricultural pest detection model founded on a lightweight approach, thus improving the efficiency of pest detection while also reducing the model’s dimensions.The model incorporates a novel grouping atrous spatial pyramid pooling fast module with four convolution layers to enhance multi-scale pest feature representation, aiming for improved detection accuracy. Additionally, it utilizes a convolutional block attention module to reduce noise and complexity in background images, facilitating the extraction of more refined and smoother pest f

APA, Harvard, Vancouver, ISO, and other styles

7

Yin, Jianjun, Pengfei Huang, Deqin Xiao, and Bin Zhang. "A Lightweight Rice Pest Detection Algorithm Using Improved Attention Mechanism and YOLOv8." Agriculture 14, no. 7 (2024): 1052. http://dx.doi.org/10.3390/agriculture14071052.

Full text

Abstract:

Intelligent pest detection algorithms are capable of effectively detecting and recognizing agricultural pests, providing important recommendations for field pest control. However, existing recognition models have shortcomings such as poor accuracy or a large number of parameters. Therefore, this study proposes a lightweight and accurate rice pest detection algorithm based on improved YOLOv8. Firstly, a Multi-branch Convolutional Block Attention Module (M-CBAM) is constructed in the YOLOv8 network to enhance the feature extraction capability for pest targets, yielding better detection results.

APA, Harvard, Vancouver, ISO, and other styles

8

Elci, Brundha, and Moulyashree S. "Pest Detection System for Farmers." International Research Journal of Computer Science 12, no. 04 (2025): 171–76. https://doi.org/10.26562/irjcs.2025.v1204.10.

Full text

Abstract:

This paper presents lightweight web-based pest detection software that aids in the early detection of crop pests using image classification techniques. The system is designed to support real-time predictions, user-friendly dashboards, and access control based on role-based logins. Built using React.js, Node.js, and MySQL, it can support up to 1000 user records with high efficiency. The software also integrates graphical visualizations using Recharts, helping users track pest prediction history and class distribution with confidence levels. This solution aims to improve agricultural productivit

APA, Harvard, Vancouver, ISO, and other styles

9

Liu, Dayang, Feng Lv, Jingtao Guo, Huiting Zhang, and Liangkuan Zhu. "Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning." Forests 14, no. 7 (2023): 1484. http://dx.doi.org/10.3390/f14071484.

Full text

Abstract:

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of

APA, Harvard, Vancouver, ISO, and other styles

10

Huang, Yiqi, Zhenhao Liu, Hehua Zhao, et al. "YOLO-YSTs: An Improved YOLOv10n-Based Method for Real-Time Field Pest Detection." Agronomy 15, no. 3 (2025): 575. https://doi.org/10.3390/agronomy15030575.

Full text

Abstract:

The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge comp

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Pest detection"

1

Leal, Soraya Cristina de Macedo. "Detection and characterization of Metarhizium anisopliae using molecular markers." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307762.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Dufresne, Philippe J. "Development and validation of molecular markers for the detection of disease resistance alleles in Lactuca sativa." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=78352.

Full text

Abstract:

In this study, RAPD (Randomly Amplified Polymorphic DNA) and SCAR (Sequence Amplified Characterized Region) markers found within 5 centiMorgans of known disease resistance loci in L. sativa were tested for their potential use in MAS. Out of thirty RAPD and SCAR markers evaluated, ten were found to be reliable predictors of disease resistance or susceptibility across a wide range of commercial and reference cultivars. Direct sequencing of seven selected markers did not reveal any significant similarity with known sequences. Three SNPs (Single Nucleotide Polymorphism) associated with two

APA, Harvard, Vancouver, ISO, and other styles

3

Steel, Roderic James. "The influence of temperature and introduction point on the detection of Rhyzopertha dominica in stored grain." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/61058/1/Roderic_Steel_Thesis.pdf.

Full text

Abstract:

The presence of insect pests in grain storages throughout the supply chain is a significant problem for farmers, grain handlers, and distributors world-wide. Insect monitoring and sampling programmes are used in the stored grains industry for the detection and estimation of pest populations. At the low pest densities dictated by economic and commercial requirements, the accuracy of both detection and abundance estimates can be influenced by variations in the spatial structure of pest populations over short distances. Geostatistical analysis of Rhyzopertha dominica populations in 2 and 3 dimens

APA, Harvard, Vancouver, ISO, and other styles

4

Shayler, Simon Paul. "Molecular detection of predation : the effects of detritivore diversity and abundance on pest control by generalist predators." Thesis, Cardiff University, 2005. http://orca.cf.ac.uk/55381/.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Muwonge, Abubaker. "Detection Of Genetically Modified Potatoes By The Polymerase Chain Reaction." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605783/index.pdf.

Full text

Abstract:

Quite a number of important crops have been genetically modified with genes for agronomically important traits, such as insect and viral resistance. As the numbers of genetically modified foods continue to increase on the market, the need for rapid development of GMO detection methods is indispensable. This study was carried out to detect if genetically modified potatoes exist on food market in Turkey. Thirty samples from different places were collected. Using a DNA based PCR method, potato samples were examined for the presence of 35S promoter, Nos terminator, neomycin phosphotransferase

APA, Harvard, Vancouver, ISO, and other styles

6

Heard, Edward. "Establishment of blackberries and detection and management of raspberry crown borer." Master's thesis, Mississippi State : Mississippi State University, 2006. http://library.msstate.edu/etd/show.asp?etd=etd-12012006-133945.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Stanaway, Mark Andrew. "Hierarchical Bayesian models for estimating the extent of plant pest invasions." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/40852/1/Mark_Stanaway_Thesis.pdf.

Full text

Abstract:

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involv

APA, Harvard, Vancouver, ISO, and other styles

8

Venter, Jan Hendrik. "Pest risk assessment for regulatory control of Bactrocera invadens (Diptera: Tephritidae) in the Musina area (Limpopo Province) / J.H. Venter." Thesis, North-West University, 2013. http://hdl.handle.net/10394/9233.

Full text

Abstract:

Fruit flies (Tephritidae) can enter and establish in new territories due to the movement of fruit from one area to another through trade or tourism, which can negatively impact on fruit production and market access. An invader fruit fly species (Bactrocera invadens) has established on the African continent and has spread throughout sub-Saharan Africa. This newly described polyphagous fruit fly species is a successful invader species which continues to distribute and establish in new habitats. The introduction and establishment of B. invadens in South Africa may have serious market access conse

APA, Harvard, Vancouver, ISO, and other styles

9

Basnet, Kumar. "Enzyme-based detection of pesticide tolerance in the sucking tea pest, helopeltis theivora waterhouse (insecta: heteroptera: miridae) with a study on bio-ecological aspects of its common spider predator from the terai tea plantations of Darjeeling foothills and plains." Thesis, University of North Bengal, 2017. http://hdl.handle.net/123456789/2626.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Tisler, Anne Marie. "Investigations of Colorado potato beetle [Leptinotarsa decemlineata (Say)] pest management including: sampling strategies for insecticide resistance detection, development of a knowledge-based expert system and the physiology of cold tolerance." Diss., Virginia Tech, 1991. http://hdl.handle.net/10919/39930.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Pest detection"

1

United States. Animal and Plant Health Inspection Service. Plant Protection and Quarantine Programs., ed. Exotic pest detection manual. APHIS Plant Protection and Quarantine, 1986.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Aerial Pest Detection and Monitoring Workshop (1994 Las Vegas, Nev.). Proceedings: Aerial Pest Detection and Monitoring Workshop, April 26-29. 1994, Las Vegas, Nevada. USDA Forest Service, Forest Pest Management, Northern Region, 1995.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

United States. Animal and Plant Inspection Service, ed. Fruit fly worm watch: Turn in a suspect, keep out a pest. U.S. Dept. of Agriculture, Animal and Plant Health Inspection Service, 1995.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

H, LaGasa Eric, Washington (State). Dept. of Agriculture. Laboratory Services Division., and Washington State Library. Electronic State Publications., eds. 2001 Western Washington exotic pest detection survey: A pheromone-trap survey for proeulia spp. (lepidoptera: tortricidae). Laboratory Services Division, Pest Program, Washington State Dept. of Agriculture, 2001.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

H, LaGasa Eric, Washington (State). Plant Protection Division., and Washington State Library. Electronic State Publications., eds. 2002 pheromone-trap detection survey for leek moth, acrolepiopsis assectella (Zeller, 1893) (lepidoptera: acrolepiidae), an exotic pest of allium spp. Plant Protection Divison, Pest Program, Washington State Dept. of Agriculture, 2003.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

H, LaGasa Eric, Washington (State). Plant Protection Division., and Washington State Library. Electronic State Publications., eds. 2002 pheromone-trap detection survey for plum fruit moth, grapholita funebrana (Treitschke, 1835) (lepidoptera: tortricidae), an exotic pest of prunus spp. Plant Protection Divison, Pest Program, Washington State Dept. of Agriculture, 2003.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

LaGasa, Eric H. 2002 light-trap detection survey for European chafer, rhizotrogus majalis (raz.) (coleoptera: scarabeidae), a turf and grain pest recently found in B.C., Canada. Plant Protection Divison, Pest Program, Washington State Dept. of Agriculture, 2003.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

United States. Animal and Plant Health Inspection Service. The Cooperative Agricultural Pest Survey: Detecting plant pests and weeds nationwide. USDA APHIS, 2005.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Goldish, Meish. Pest-sniffing dogs. Bearport Pub., 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Fitzhugh, Bill. Pest control. Avon Books, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Pest detection"

1

Nandhini, C., and M. Brindha. "Deep Learning Solutions for Pest Detection." In Object Detection with Deep Learning Models. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736-10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Gaikwad, Sukanya S., and Mallikarjun Hangarge. "Pest Detection System for Rice Crop Using Pest-Net Model." In Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-196-8_45.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Rakhonde, Vinith, K. Srujan Raju, Nuthanakanti Bhaskar, and A. Raji Reddy. "Automated Pest Detection Using Image Classification." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9442-7_68.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Costa, Dinis, Catarina Silva, Joana Costa, and Bernardete Ribeiro. "Improving Pest Detection via Transfer Learning." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49249-5_8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Nayar, Pratibha, Shivank Chhibber, and Ashwani Kumar Dubey. "Implementation of YOLOv7 for Pest Detection." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34222-6_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Wang, Rujing, Lin Jiao, and Kang Liu. "Crop Pest Detection Methods in Field." In Deep Learning for Agricultural Visual Perception. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4973-1_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Kanniah, Kasturi, and Le Yu. "Oil Palm Pest and Disease Detection." In Geospatial Technology for Sustainable Oil Palm Industry. CRC Press, 2024. http://dx.doi.org/10.1201/9780429199813-10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Sujaritha, M., M. Kavitha, and S. Roobini. "Pest Detection Using Improvised YOLO Architecture." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7169-3_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Amara, Santosh Jayanth, S. Yamini, and D. Sumathi. "Pest Detection Using YOLO V7 Model." In Data Science and Network Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6755-1_17.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Mcgee, Denis C. "Detection and Diagnosis of Soybean Diseases for Improved Management." In Pest Management in Soybean. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2870-4_24.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Pest detection"

1

V, Rajeshram, Najeer Ahamed A, Raghul R, Rajalingam M, and Vengadesan M. C. "Smart Pest Control System: Deep Learning Algorithms for Pest Detection and Pesticide Selection." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866708.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Xiong, Hui Jun, Xiao Qin Chen, and Hong Xie. "Improved forest pest detection based on YOLOv8." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049799.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Tani, Ishrat Zahan, and Tasmia Jannat. "Effective Pest Detection by YOLOv5 with BottleneckCSP." In 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON). IEEE, 2024. https://doi.org/10.1109/peeiacon63629.2024.10800369.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Elsayed, Mohamed Z., Ali Hasoon, Mohamed K. Zidan, and Sarah M. Ayyad. "Role of AI for Plant Disease Detection and Pest Detection." In 2024 International Telecommunications Conference (ITC-Egypt). IEEE, 2024. http://dx.doi.org/10.1109/itc-egypt61547.2024.10620496.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Chen, Xiaoqin, Hong Xie, and Ni Jiang. "Pest detection method based on improved YoLoV8 model." In 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), edited by Xin Zhang and Minghao Yin. SPIE, 2024. http://dx.doi.org/10.1117/12.3048446.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Rui, Zizheng, Yuhang He, Wenxuan Yang, Hongxuan Xu, Yadong Xie, and Jiayi Wang. "Pest Rat Detection and Capture System in Barn." In 2024 International Conference on Intelligent Robotics and Automatic Control (IRAC). IEEE, 2024. https://doi.org/10.1109/irac63143.2024.10871849.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Tang, Yeda, Shukai Duan, and Lidan Wang. "EC-YOLO: Enhanced YOLOv10 for Agricultural Pest Detection." In 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2025. https://doi.org/10.1109/iccece65250.2025.10984447.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Karthik, CH Bharadwaj, B. Lakshmi Dhevi, and Levi Vicliff SJ. "Deep Learning-based Intelligent Pest Detection and Management." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11081359.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Yi, Zhoujian, Zijie Huo, Yuqi Lai, Zhjian Yin, Jun Li, and Zhen Yang. "An improved YOLOv8-based pest detection model for detecting elongate larvae." In Fourth International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2024), edited by Hoshang Kolivand and Ata Jahangir Moshayedi. SPIE, 2024. http://dx.doi.org/10.1117/12.3044869.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ilakya, T., B. Lakshmi, S. Jeyanthi, U. Esakkiammal, T. Abinaya, and C. F. Theresa Cenate. "Towards Resilient Crops: Disease Detection and Pest Control Strategies." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780121.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Pest detection"

1

Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

Full text

Abstract:

Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were test

APA, Harvard, Vancouver, ISO, and other styles

2

Pomeroy, Robert, and Ryan Simkovsky. Integrated Pest Management (IPM) for Early Detection Algal Crop Protection (Final Report). Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1862344.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Herrera C., Lorena, Laura Villamizar R., and Juliana Gómez V. Development of an immunological technique for detecting granulovirus infection in Tuta absoluta larvae (Lepidoptera: Gelechiidae). Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, 2012. http://dx.doi.org/10.21930/agrosavia.poster.2012.12.

Full text

Abstract:

Tuta absoluta (Meyrick, 1917) (Lepidoptera: Gelechiidae), known as tomato moth or tomato leafminer is a microlepidopter from Gelechiidae’s family, which is widely distributed on America, Europe, Africa and Asia and is considered the most important pest of this crop (Roditakis et al. 2010). Phthorimaea operculella granulovirus (PhopGV) has been used for controlling larvae of different moths from Gelechiidae’s family as Tecia solanivora and P. operculella in several countries of South America as Colombia, Brazil, Argentina and Peru, and probably can also be pathogenic for T. absoluta larvae. How

APA, Harvard, Vancouver, ISO, and other styles

4

Mizrach, Amos, Michal Mazor, Amots Hetzroni, et al. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

Full text

Abstract:

This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasio

APA, Harvard, Vancouver, ISO, and other styles

5

Young, Craig. Problematic plant monitoring in Pea Ridge National Military Park: 2006–2021. Edited by Tani Hubbard. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293656.

Full text

Abstract:

Managers are challenged with the impact of problematic plants, including exotic, invasive, and pest plant species. Information on the cover and frequency of these plant species is essential for developing risk-based approaches to managing them. Based on surveys conducted in 2006, 2013, 2018, and 2021, Heartland Inventory and Monitoring Network staff and contractors identified a cumulative total of 38 potentially problematic plant species in Pea Ridge National Military Park. Of the 35 species found in 2021, we characterized 13 as very low frequency, 9 as low frequency, 9 as medium frequency, an

APA, Harvard, Vancouver, ISO, and other styles

6

Karp, Joel. High-Resolution PET Detector. Final report. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1124656.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Garg, Pradeep K. Use of PET Imaging for Early Detection of Ovarian Carcinoma. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada542182.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Michaels, Trevor. Red-tailed boa (Boa constrictor) surveys at Salt River Bay National Park, St. Croix U.S. Virgin Islands: 2023 report of activities. National Park Service, 2024. http://dx.doi.org/10.36967/2303799.

Full text

Abstract:

St. Croix is home to a variety of threatened and endangered (T&E) species that are at risk for predation by the invasive red-tailed boa (Boa constrictor), such as the St. Croix ground lizard (Amevia polyps), the ground-nesting least tern (Sterna antillarum), and the hawksbill sea turtle (Eretmochelys imbricata). Genetic analysis determined the original red-tailed boa population on St. Croix sourced from a single female released by a pet owner and its range expands every year. Presently, the main population of red-tailed boa is established on the west end of St. Croix and extends as far eas

APA, Harvard, Vancouver, ISO, and other styles

9

Sun, Xiankai. Specific PET Imaging Probes for Early Detection of Prostate Cancer Metastases. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535575.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Sun, Xiankai. Specific PET Imaging Probes for Early Detection of Prostate Cancer Metastases. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada562173.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!