Relevant bibliographies by topics / Weed identification

Academic literature on the topic 'Weed identification'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Weed identification"

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Bushra Idrees. "Weed Identification Methodology by using Transfer Learning." Lahore Garrison University Research Journal of Computer Science and Information Technology 5, no. 2 (June 21, 2021): 28–39. http://dx.doi.org/10.54692/lgurjcsit.2021.0502206.

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From recent past years, Weed identification remained a hot topic for researchers. Majority of work focused on the detection of weed but we are trying to identify the weed via weed name. The unrivaled successes of deep learning make the researchers able to evaluate different weed species in the complex rangeland climate. Nowadays, with an increasing population, farming productivity needs to be increased a lot to meet the demand for accurate weed detection. Increased demand for an increase in the use of herbicides, resulting in environmental harm. In this research work, the picture of weed helps to detect and differentiate as per area, and its name. The main aim of this research is the identification of weed so that fewer herbicides can use. This research work will contribute toreducing the higher use of herbicides by helping clear identification of weed names through its features. We use transfer learning in machine learning. The deep Weeds dataset is used for the evaluation. For this, we use the deep learning model ResNet50 to get better results. The Deep Weeds dataset contains 17,509 images that are label and eight nationally recognized species of weed belonged to 8 across northern Australia locations. This paper declares a baseline for classification performance on the dataset of weed while utilizing the deep learning model ResNet-50 and it is a benchmark too. Deep learning model ResNet-50 attained an average accuracy classification of 96.16. The findings are high enough to make effective use of weed control methods in Pakistan for futurefield implementation. The results confirm that our System offers more effective Weed recognition than many other systems.
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Werle, Isabel Schlegel, Edicarlos Castro, Carolina Pucci, Bhawna Soni Chakraborty, Shaun Broderick, and Te Ming Tseng. "Identification of Weed-Suppressive Tomato Cultivars for Weed Management." Plants 11, no. 3 (February 2, 2022): 411. http://dx.doi.org/10.3390/plants11030411.

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Weed-suppressive crop cultivars are a potentially attractive option in weed management strategies (IWM). A greenhouse study was conducted at the R. R. Foil Plant Science Research Center, Starkville, MS, to assess the potential weed-suppressive ability of 17 tomato cultivars against Palmer amaranth (Amaranthus palmeri S. Wats), yellow nutsedge (Cyperus esculentus L.), and large crabgrass (Digitaria sanguinalis L.). The experiment was a completely randomized design, with four replications, and was repeated twice. The height, chlorophyll, and dry weight biomass of the weeds were measured 28 days after sowing. Weed suppression varied greatly among tomato cultivars. The most significant effect of tomato interference was recorded on Palmer amaranth, and the least reduction was observed with yellow nutsedge plants. Cultivars 15 and 41 reduced Palmer amaranth height and biomass by about 45 and 80%, respectively, while cultivar 38 reduced 60% of the chlorophyll percentage. Large crabgrass plants were 35% shorter in the presence of cultivar 38 and had a biomass reduction of 35% in the presence of cultivar 38. Under tomato interference, a minimal effect was observed in chlorophyll, height, and biomass of yellow nutsedge seedlings. Factoring all parameters evaluated, cultivars 38 and 33 were most suppressive against Palmer amaranth and large crabgrass.
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Murdock, Edward C., Judy A. Alden, DeWitt T. Gooden, and Joe E. Toler. "Weed identification field training demonstrations." Journal of Agronomic Education 15, no. 1 (March 1986): 37–40. http://dx.doi.org/10.2134/jae1986.0037.

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Schulthess, Urs, Kris Schroeder, Ahmed Kamel, AbdElGhani M. AbdElGhani, El‐Hassanein E. Hassanein, Shaban Sh AbdElHady, AbdElMaboud AbdElShafi, Joe T. Ritchie, Richard W. Ward, and Jon Sticklen. "NEPER‐Weed: A Picture‐Based Expert System for Weed Identification." Agronomy Journal 88, no. 3 (May 1996): 423–27. http://dx.doi.org/10.2134/agronj1996.00021962008800030010x.

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Zahoor, Saniya, and Shabir A.Sofi. "Weed Identification in Crop Field Using CNN." Journal of University of Shanghai for Science and Technology 23, no. 10 (October 1, 2021): 15–21. http://dx.doi.org/10.51201/jusst/21/09697.

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Since the ages agriculture has remained as the backbone of economies especially developing countries like ours, where population is growing rapidly being second most populated country in the world, food demands are increasing so, farmers need to maximize their productivity. Weed is one of the enemies to farmer’s crop which competes with the crop for nutrients and sometimes hinders the growth of crop. Weed can cause loss of production ranging from 10 to 100%. There has been research on the use of many CNN models for weed identification. This paper presents a classification model to distinguish between weed and crop images and it classifies 12 species of weeds and crops. The proposed model achieves 96.45% of accuracy during training and of 90.08% during validation and testing.
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Lindquist, John L., Peter K. Fay, and James E. Nelson. "Teaching Weed Identification at Twenty U.S. Universities." Weed Technology 3, no. 1 (March 1989): 186–88. http://dx.doi.org/10.1017/s0890037x00031596.

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The methods used to teach weed identification at 20 U.S. universities were obtained for comparison through a telephone survey in December, 1986, and January, 1987. Weed identification is taught as a portion (30%) of the laboratory section in introductory weed science courses. Only five have a separate weed identification course. Field trips frequently are used to teach weed identification. Students must learn from 50 to 125 weed species with some seedling identification. Pressed plant collections of approximately 50 weed species normally are required. Most instructors strongly suggest using live plants and repetition for long-term learning.
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Putniece, Gundega, Ingrīda Augšpole, and Inta Romanova. "Population of Weeds in a Plantation of Red Raspberries (Rubus Idaeus L.)." Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences. 76, no. 4 (August 1, 2022): 551–54. http://dx.doi.org/10.2478/prolas-2022-0085.

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Abstract Data from monitoring of weed populations are relevant for successful integrated weed management. The purpose of this experiment was to compare the diversity of weed species in red raspberry plantation rows. The red raspberry plantation was established in August 2019. The plantation is located in the south part of Latvia, Zemgale region (N56°33’29.5302”, E23°46’26.04”). The red raspberry cultivars ‘Daiga’, ‘Shahrazada’, ‘Norna’, and ‘Polana’ were grown in the plantation. The bushes were spaced at 0.6 m in rows and 3 m between rows in a plot with size 0.51 ha. Weed infestation in the red raspberry plantation was determined by using the counting method. The counting of weeds and identification of weed species were done three times during the vegetation at the 16th,22nd, and 30th week of 2021. In total, 34 weed species, including 18 annual and 16 perennial weeds were present in the red raspberry plantation. Poa annua (5–60 plants per m-2) and Elytrigia repens (6–393 plants m−2) were found as dominant weeds. During experiment there were differences in weed populations and density between red raspberry cultivars. The cultivar ‘Norna’ was dominated by perennial weeds, while ‘Polana’ by annual weeds.
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Granitto, Pablo M., Hugo D. Navone, Pablo F. Verdes, and H. A. Ceccatto. "Weed seeds identification by machine vision." Computers and Electronics in Agriculture 33, no. 2 (February 2002): 91–103. http://dx.doi.org/10.1016/s0168-1699(02)00004-2.

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Gibson, Kevin D., Richard Dirks, Case R. Medlin, and Loree Johnston. "Detection of Weed Species in Soybean Using Multispectral Digital Images." Weed Technology 18, no. 3 (September 2004): 742–49. http://dx.doi.org/10.1614/wt-03-170r1.

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The objective of this research was to assess the accuracy of remote sensing for detecting weed species in soybean based on two primary criteria: the presence or absence of weeds and the identification of individual weed species. Treatments included weeds (giant foxtail and velvetleaf) grown in monoculture or interseeded with soybean, bare ground, and weed-free soybean. Aerial multispectral digital images were collected at or near soybean canopy closure from two field sites in 2001. Weedy pixels (1.3 m2) were separated from weed-free soybean and bare ground with no more than 11% error, depending on the site. However, the classification of weed species varied between sites. At one site, velvetleaf and giant foxtail were classified with no more than 17% error, when monoculture and interseeded plots were combined. However, classification errors were as high as 39% for velvetleaf and 17% for giant foxtail at the other site. Our results support the idea that remote sensing has potential for weed detection in soybean, particularly when weed management systems do not require differentiation among weed species. Additional research is needed to characterize the effect of weed density or cover and crop–weed phenology on classification accuracies.
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Elstone, Lydia, Kin Yau How, Samuel Brodie, Muhammad Zulfahmi Ghazali, William P. Heath, and Bruce Grieve. "High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding." Sensors 20, no. 2 (January 14, 2020): 455. http://dx.doi.org/10.3390/s20020455.

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Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.
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Dissertations / Theses on the topic "Weed identification"

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Shirzadifar, Alimohammad. "Identification of Weed Species and Glyphosate-Resistant Weeds Using High Resolution UAS Images." Diss., North Dakota State University, 2018. https://hdl.handle.net/10365/29304.

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Adoption of a Site-Specific Weed Management System (SSWMS) can contribute to sustainable agriculture. Weed mapping is a crucial step in SSWMS, leads to saving herbicides and protecting environment by preventing repeated chemical applications. In this study, the feasibility of visible and near infrared spectroscopy to classify three problematic weed species and to identify glyphosate-resistant weeds was evaluated. The canopy temperature was also employed to identify the glyphosate-resistant weeds. Furthermore, the ability of UAS imagery to develop accurate weed map in early growing season was evaluated. A greenhouse experiment was conducted to classify waterhemp (Amaranthus rudis), kochia (Kochia scoparia), and lambsquartes (Chenopodium album) based on spectral signature. The Soft Independent Modeling of Class Analogy (SIMCA) method on NIR (920-2500 nm) and Vis/NIR (400-2500 nm) regions classified three different weed species with accuracy greater than 90 %. The discrimination power of different wavelengths indicated that 640, 676, and 730 nm from red and red-edge region, and 1078, 1435, 1490, and 1615 nm from the NIR region was the best wavelengths for weed species discrimination. While, wave 460, 490, 520 and 670 nm from Vis range, and 760, 790 nm from NIR region were the significant discriminative features for identifying glyphosate-resistant weeds. Random Forest was able to detect glyphosate-resistant weeds based on spectral weed indices with more than 95% accuracy. Analysis of thermal images indicated that the canopy temperature of glyphosate-resistant weeds was less than susceptible ones early after herbicide application. The test set validation results showed the support vector machine method could classify resistant weed species with accuracy greater than 95 %. Based on the stepwise method the best times for discrimination of kochia, and waterhemp resistant were 46 and 95 hours after glyphosate application, respectively. In addition, a field study was proposed on soybean field to identify weed species and glyphosate-resistant weeds using multispectral and thermal imagery. Results revealed that the object-based supervised classification method could classify weed species with greater than 90% accuracy in early growing season. Furthermore, the glyphosate-resistant kochia, waterhemp and ragweed were identified based on canopy temperature with 88%, 93% and 92% accuracy, respectively.
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Smith, Carey. "Studies on weed risk assessment." Title page, contents and abstract only, 1999. http://web4.library.adelaide.edu.au/theses/09AFM/09afms644.pdf.

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Bibliography: leaves 124-136. This thesis gives an overview of factors used in weed risk assessments and explores the disparity between the measured high accuracy rate of the weed risk assessment system (WRA) as implemented in Australia and the pessimistic assessments of some workers about the possibility of predicting the weed potential of plant species imported in the future. The accuracy of the WRA may not be as high as previously thought, and it varies with weed definition and taxonomic groups. Cluster analysis and comparative analysis by independent contrasts were employed to determine the value of individual biological and ecological questions on the WRA questionnaire. Results showed that some WRA questions could be deleted from the questionnaire and the scores for others weighted differently. The WRA is not a reliable predictor of weeds when it is considered in the context of the base-rate probability of an introduced plant becoming weedy in Australia. As a result a far greater number on non-weeds will be placed on the prohibited imported list than was initially expected.
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Suresh, Babu Dharani. "Plant-Stand Count and Weed Identification Mapping Using Unmanned Aerial Vehicle Images." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/29271.

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Modern agriculture encounters several challenges these days. There is a vital need for spatial data about plant and weed distributions. Obtaining accurate knowledge of the plants and weeds distribution in the field with manual methods are time-consuming. In this research, image processing programs were developed from the unmanned aerial vehicle (UAV) digital images to obtain the plant-stand count and weed identification and mapping in the field. Algorithms using pixel-march with search-hands criterion for the plant-stand count and shape-based features for weed identification were developed. Results were found to be accurate in the cropped UAV stitched images (>99 %) in manual image-based validation. User-friendly message windows, labeled images, textual results, and distribution maps were produced as outputs. The outcomes of this study will enable farmers to determine the plant and weed distributions in the field and will be helpful in deploying precision agriculture measures.
Cannayen, Igathinathane
Flores, Joao Paulo
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Paap, Arie Jacobus. "Development of an optical sensor for real-time weed detection using laser based spectroscopy." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2014. https://ro.ecu.edu.au/theses/1282.

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The management of weeds in agriculture is a time consuming and expensive activity, including in Australia where the predominant strategy is blanket spraying of herbicides. This approach wastes herbicide by applying it in areas where there are no weeds. Discrimination of different plant species can be performed based on the spectral reflectance of the leaves. This thesis describes the development of a sensor for automatic spot spraying of weeds within crop rows. The sensor records the relative intensity of reflected light in three narrow wavebands using lasers as an illumination source. A prototype weed sensor which had been previously developed was evaluated and redesigned to improve its plant discrimination performance. A line scan image sensor replacement was chosen which reduced the noise in the recorded spectral reflectance properties. The switching speed of the laser sources was increased by replacing the laser drivers. The optical properties of the light source were improved to provide a more uniform illumination across the viewing area of the sensor. A new opto-mechanical system was designed and constructed with the required robustness to operate the weed sensor in outdoor conditions. Independent operation of the sensor was made possible by the development of hardware and software for an embedded controller which operated the opto-electronic components and performed plant discrimination. The first revised prototype was capable of detecting plants at a speed of 10 km/h in outdoor conditions with the sensor attached to a quad bike. However, it was not capable of discriminating different plants. The final prototype included a line scan sensor with increased dynamic range and pixel resolution as well as improved stability of the output laser power. These changes improved the measurement of spectral reflectance properties of plants and provided reliable discrimination of three different broadleaved plants using only three narrow wavelength bands. A field trial with the final prototype demonstrated successful discrimination of these three different plants at 5 km/h when a shroud was used to block ambient light. A survey of spectral reflectance of four crops (sugarcane, cotton, wheat and sorghum) and the weeds growing amongst these crops was conducted to determine the potential for use of the prototype weed sensor to control spot-spraying of herbicides. Visible reflectance spectra were recorded from individual leaves using a fibre spectrometer throughout the growing season for each crop. A discriminant analysis was conducted based on six narrow wavebands extracted from leaf level spectral reflectance measured with a spectrometer. The analysis showed the potential to discriminate cotton and sugarcane from
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Dang, Kim Son Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. "Design and control of autonomous crop tracking robotic weeder : GreenWeeder." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2009. http://handle.unsw.edu.au/1959.4/44418.

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This thesis reports the design and control of the ??GreenWeeder??, a non-herbicidal autonomous weeding robot, in order to autonomously track crop rows for weeding through electrocution in the inter-row space. The four wheel mobile robot platform was designed and built with a motorised Ackerman steering system allowing the robot to steer up to 30 degree left and right. It was also equipped with an electronically geared rear wheel drive, a pair of stereo cameras, a SICK LMS-291 laser range finder to localize itself with respect to the crop rows, a GPS system for obtaining the robot position in the field and a long-range communication system for tele-supervision by operators. The first prototype of the robot electrocution system was also designed and constructed to ignite 22kV electrical arcs to destroy weeds. Its operation was tested in the research field of the University of Sydney and the results of this experiment were analysed to improve the efficiency of this first prototype. An improved prototype of the electrocution system was then constructed and attached to a cradle extending out at the back of the mobile robot platform. The testing of this improved prototype was conducted at Lansdowne farm, a research field of the University of Sydney. After the construction of the robot platform, the robot control was considered with the demands for robot localization with respect to crop rows, an autonomously tracking control system and a manual control mode in order to take the robot to transportation vehicles. Firstly, the robot localization was accomplished by utilizing SICK LMS-291 laser range finder sensor for the sensing and perception of the robot. Secondly, the robot control system was developed with a PID controller, a second order model of the robot system and a first order filter. The PID controller is in the standard form with the filtered derivative and the PI part being in automatic reset configuration. The second order model was identified using Matlab System Identification toolbox based on the robot kinematic analysis. The first order filter is utilized for filtering out the lateral deviations of the robot with respect to the crop rows received from the SICK laser sensor. A Simulink simulation model of the robot control system was also built in order to fine-tune PID and filter parameters. Thirdly, the manual control mode of the robot was produced. In this mode, a joystick can be attached to a notebook to wirelessly drive the robot around or it can be plugged into a USB port at the back of the robot to drive it without the notebook. After the robot control was implemented and simulated, some experiments were conducted with the robot autonomously tracking a strip of reflective tape mimicking a crop row stuck into the ground of a laboratory. Depending on distances from the row assigned to the controller, the robot tried to keep those distances away from the row. The results showed the lateral errors of the robot with respect to the row were approximately 4.5 cm which were sufficient for our current agricultural application.
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Triolet, Marion. "Identification et caractérisation de candidats d'origine naturelle à action herbicide pour contrôler les adventices." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCK032.

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Un projet visant à identifier des mycoherbicides pour lutter contre les adventices a été initié entre l’UMR Agroécologie de Dijon et la société DE SANGOSSE® (Agen). Trois volets ont structuré ce projet à l’issue d’une collecte de prélèvement de 475 plantes représentatives de 23 espèces d’adventices symptomatiques et asymptomatiques en Bourgogne et en Beauce. Le 1er volet reposait sur une approche de type metabarcoding (technologie Illumina), pour évaluer et comparer la diversité des communautés fongiques endophytes des plantes symptomatiques et asymptomatiques. 542 genres fongiques ont ainsi été identifiés. Des taxons associés aux plantes symptomatiques ont été identifiés. Parmi ceux-ci, certains sont des pathogènes connus, d’autres non et ils constituent des pistes à exploiter pour la recherche de candidats mycoherbicides. Le deuxième volet repose sur une approche conventionnelle de microbiologie et pathologie. Une collection de 194 champignons associés aux symptômes des adventices a été constituée. La pathogénicité de ces isolats a été testée grâce à une série de screenings de plus en plus sélectifs qui ont abouti à la sélection de cinq souches, identifiées par séquençage de l’ITS ou d’autres marqueurs taxonomiques. Une souche appartient à l’espèce Boeremia exigua var exigua, une autre à l’espèce Alternaria alternata, deux appartiennent à l’espèce A. penicillata et la dernière au genre Alternaria. Le troisième volet visait à identifier le mode d’action d’une souche par une double approche, métabolomique et microscopique. La souche de B. exigua var exigua secrète des métabolites phytotoxiques mais également infeste et semble détruire les tissus végétaux sous-épidermique de la plante hôte.Ce projet exploratoire a fourni des pistes de taxons fongiques associés à des symptômes observés sur adventices en analysant la diversité par une approche moléculaire et a fourni des souches fongiques, mycoherbicides potentiels, par une approche microbiologique dont on voit bien qu’elle reste une méthode incontournable, malgré ses limites, pour obtenir des candidats fongiques à action herbicide
A project aiming at identifying mycoherbicides to control weeds has been initiated between the UMR Agroécologie (Dijon) and the company DE SANGOSSE® (Agen, France). Three axes structured this project after a sampling collection of 475 plants representative of 23 species of symptomatic and asymptomatic weeds was carried out in Burgundy and Beauce. The first part was based on a metabarcoding approach (Illumina technology), to evaluate end compare the diversity of endophytic fungi communities of symptomatic and asymptomatic weeds. 542 fungal genera have been identified. Taxa associated with symptomatic plants have been identified. Of these, some are known pathogens, others are not, and both constitute avenues to exploit for the research of mycoherbicide candidates. The second axe is based on a conventional approach to microbiology and pathology. A collection of 194 fungi associated with weed symptoms was established. The pathogenicity of these isolates was tested through a series of increasingly selective screenings that resulted in the selection of five strains that were identified by sequencing of ITS or other taxonomic markers. One strain belongs to the species Boeremia exigua var exigua, another species Alternaria alternata, two belong to the species A. penicillata and the last to the genus Alternaria. The third axe aimed at identifying the mode of action of a strain by a dual metabolomics and microscopic approach. The strain of B. exigua var exigua produced phytotoxic secondary metabolites but also infested and apparently destroyed the sub-epidermal plant tissues of the host plant.This exploratory project provided tracks to exploit fungal taxa associated with observed weeds symptoms, by analyzing the diversity, by a molecular approach and provided fungal strains, potential mycoherbicides by a conventional microbiological approach that we can see it remains an unavoidable method, despite its limitations, to obtain fungal candidates with herbicidal action
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Pernomian, Viviane Araujo. "Identificação de plantas invasoras em tempo real." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12022003-123905/.

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A identificação de plantas invasoras é de extrema importância em diversos procedimentos utilizados na agricultura. Apesar de ser uma tarefa computacionalmente difícil, esta identificação tem se tornado muito importante no contexto da agricultura de precisão. A agricultura de precisão substitui os tratos culturais de grandes áreas da cultura, feitos pela média do nível dos problemas encontrados nessas áreas, por tratamento específicos e pontuais. As pricipais vantagens são o aumento de produtividade, relacionado com a diminuição da variabilidade na produção, a economia de insumos e a preservação do meio ambiente. Este trabalho enfoca o reconhecimento de plantas invasoras em tempo real. Para manter o requisito de tempo real, são utilizadas redes neurais artificiais como meio para o reconhecimento de padrões. Entre as diversas plantas invasoras de ocorrência freqüente no cerrado brasileiro, foi selecionado o picão preto para a avaliação das técnicas adotadas. Uma arquitetura modular de reconhecimento é proposta, com o uso de processamento paralelo, facilitando a inclusão de módulos de reconhecimento de outras plantas invasoras sem a deterioração do desempenho do sistema. Os resultados obtidos são amplamente satisfatórios, demonstrando a possibilidade do desenvolvimento de um sistema embarcado completo de identificação de plantas invasoras em tempo real. Este sistema, apoiado pelo sistema de posicionamento global GPS, pode servir de base para uma série de máquinas agrícolas inteligentes, como pulverizadores de herbicidas e outros defensivos utilizados na agricultura.
Weed identification is an important task in many agricultural procedures. In spite of being a computation intensive task, this identification is very important in the role of precision agriculture. Conventional procedures in agriculture are based on the average level of the problems found in large areas. Precision agriculture introduces new punctual management procedures, dealing with very small areas. The main advantages are: productivity increase, related with the decrease in production unevenness, economy and environment preservation. This work focuses on the real time recognition of weeds. To maintain the real time requirement, neural networks are used to carry out the recognition of image patterns. Among the several weeds frequently found in the Brazilian savannah, the "picão preto" was selected for the evaluation of the adopted techniques. A modular architecture is proposed, using parallel processing, making easier the use of new recognition modules (for other weeds), still preserving the real time capabilities of the system. Results obtained are thoroughly adequate, demonstrating the possibility of the development of embedded systems for the identification of several weeds in real time. These systems, jointly with the global positioning system (GPS), can be used in a family of intelligent equipment, such as spraying machines for herbicides and other agricultural products.
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Li, Chao. "WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/133.

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Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms. A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern is captured which contains all the information about the penetration status. An experienced welder is able to determine weld penetration status just based on the reflected laser pattern. However, it is difficult to characterize the images to extract key information that used to determine penetration status. To overcome the challenges in finding right features and accurately processing images to extract key features using conventional machine vision algorithms, we propose using convolutional neural network (CNN) to automatically extract key features and determine penetration status. Data-label pairs are needed to train a CNN. Therefore, an image acquiring system is designed to collect reflected laser pattern and the image of work-piece backside. Data augmentation is performed to enlarge the training data size, which resulting in 270,000 training data, 45,000 validation data and 45,000 test data. A six-layer convolutional neural network (CNN) has been designed and trained using a revised mini-batch gradient descent optimizer. Final test accuracy is 90.7% and using a voting mechanism based on three consequent images further improve the prediction accuracy.
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Anbalagan, Selvakumar. "Identification of novel strategies to radiosensitise tumour cells." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:e826a95f-7a16-401d-827c-5afc8003b924.

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In this study we found that tumour cells can be radiosensitised by targeting the DNA damage response kinases, ATM and ATR. Furthermore, we highlight that Wee1 inhibitors, which are already under the clinical trials need to be further investigated in combination with radiation in the context of tumour hypoxia. In addition, we observed that induction of autophagy using STF-62247 can lead to radiosensitisation of VHL deficient RCC cells. Our studies with the rapamycin analogue temsirolimus, already in the clinic for the treatment of various cancers, can be a potential candidate as a radiosensitiser for RCC cells. Overall, these finding led us to investigate further whether autophagy inducing compounds, which are either in clinic or in clinical trials, can effect the response to radiation. From a panel of candidate drugs which are known to induce autophagy we identified an aminopeptidase inhibitor, CHR-2797. CHR-2797 induces autophagy in the oesophageal cancer cell lines FLO-1 and OE21. Although, our results with CHR-2797 demonstrate it as a potential radiosensitiser, the mechanism of its radiosensitisation needs to be established. Our results from CHR-2797-induced radiosensitisation, further led us to investigate if other aminopeptidase inhibitors have a role in radiosensitisation. Therefore, we selectively screened candidate aminopeptidase inhibitors and identified some promising effects on radiosensitivity.
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Legleiter, Travis R. "Identification, characterization, and management of glyphosate-resistant waterhemp (Amaranthus rudis sauer.) in Missouri." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5620.

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Thesis (M.S.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on July 8, 2009) Includes bibliographical references.
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Books on the topic "Weed identification"

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Southern Weed Science Society (U.S.). Weed Identification Committee. Weed identification guide. Edited by Elmore C. Dennis 1940-. Champaign, IL: Southern Weed Science Society, 1985.

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University of California (System). Cooperative Extension. The grower's weed identification handbook. 8th ed. [Berkeley, Calif.]: University of California, Division of Agriculture and Natural Resources, 1996.

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Alberta. Alberta Agriculture, Food, and Rural Development. Weed seedling guide. Edmonton, Alberta: Alberta Agriculture, Food and Rural Development, 1996.

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Johnston, William J. Lawn weed control for Washington State homeowners. 2nd ed. [Pullman, Wash.]: Cooperative Extension, Washington State University, 2002.

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Johnston, William J. Lawn weed control for Washington state homeowners. [Pullman, Wash.]: Cooperative Extension, Washington State University, 1999.

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Intyre, G. Mc. Weeds of sugar cane in Mauritius: Their description and control. Réduit, Mauritius: Mauritius Sugar Industry Research Institute, 1991.

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Kaul, Maharaj Krishen. Weed flora of Kashmir Valley. Jodhpur, India: Scientific Publishers, 1986.

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Division, Montana Environmental Management. Weed training manual. Helena, Mont: The Division, 1986.

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1955-, Carr Anna, Brickman Robin, and Rodale Press, eds. Rodale's garden insect, disease & weed identification guide. Emmaus, Pa: Rodale Press, 1988.

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Colquhoun, Jed. Pacific Northwest's least wanted list: Invasive weed identification and management. [Corvallis, Or.]: Oregon State University Extension Service, 2003.

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Book chapters on the topic "Weed identification"

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Rogers, Garry. "Desert Weed Identification." In Desert Weeds, 27–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45854-6_8.

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Karthikeyan, P., M. Manikandakumar, D. K. Sri Subarnaa, and P. Priyadharshini. "Weed Identification in Agriculture Field Through IoT." In Advances in Intelligent Systems and Computing, 495–505. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5029-4_41.

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Liu, Shengping, Junchan Wang, Liu Tao, Zhemin Li, Chengming Sun, and Xiaochun Zhong. "Farmland Weed Species Identification Based on Computer Vision." In Computer and Computing Technologies in Agriculture XI, 452–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06137-1_41.

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Ni, Chenggong, Yanshan Yang, Yan Sun, Bin Tian, and Tianquan Liu. "Multi-kernel Non-convex Optimized Weed Identification Method." In Lecture Notes in Electrical Engineering, 1338–44. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_141.

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Thompson, B. M., M. M. Kirkpatrick, D. C. Sands, and A. L. Pilgeram. "Pseudomonas syringae: Prospects for Its Use as a Weed Biocontrol Agent." In Pseudomonas syringae Pathovars and Related Pathogens – Identification, Epidemiology and Genomics, 117–24. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6901-7_14.

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Wang, Jingxian, Miao Li, Jian Zhang, WeiHui Zeng, and XuanJiang Yang. "DCNN Transfer Learning and Multi-model Integration for Disease and Weed Identification." In Image and Graphics Technologies and Applications, 216–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9917-6_21.

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Potena, Ciro, Daniele Nardi, and Alberto Pretto. "Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture." In Intelligent Autonomous Systems 14, 105–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48036-7_9.

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Mall, Smriti, Swapna Gupta, and P. P. Upadhyaya. "Identification of Tomato Leaf Curl Virus Infecting Acalypha indica: An Ethnomedicinal Weed in North-Eastern Uttar Pradesh." In Microbial Diversity and Biotechnology in Food Security, 177–81. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1801-2_14.

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Weis, Martin, and Markus Sökefeld. "Detection and Identification of Weeds." In Precision Crop Protection - the Challenge and Use of Heterogeneity, 119–34. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9277-9_8.

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Roché, Ben F., and Cindy Talbott Roché. "Identification, Introduction, Distribution, Ecology, and Economics of Centaurea Species." In Noxious Range Weeds, 274–91. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9780429046483-28.

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Conference papers on the topic "Weed identification"

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Liang, Wei-Che, You-Jei Yang, and Chih-Min Chao. "Low-Cost Weed Identification System Using Drones." In 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW). IEEE, 2019. http://dx.doi.org/10.1109/candarw.2019.00052.

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Singher, Liviu. "Electro-optical-based machine vision for weed identification." In Photonics East (ISAM, VVDC, IEMB), edited by George E. Meyer and James A. DeShazer. SPIE, 1999. http://dx.doi.org/10.1117/12.336895.

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Pan Li, Dongjian He, Yongliang Qiao, and Chenghai Yang. "An application of soft sets in weed identification." In 2013 Kansas City, Missouri, July 21 - July 24, 2013. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2013. http://dx.doi.org/10.13031/aim.20131620222.

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Tang, Minnan, and Cheng Cai. "Weed seeds identification based on structure elements' descriptor." In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2013. http://dx.doi.org/10.1109/apsipa.2013.6694380.

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Miao, Fengiuan, Siqi Zheng, and Bairui Tao. "Crop Weed Identification System Based on Convolutional Neural Network." In 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2019. http://dx.doi.org/10.1109/iceict.2019.8846268.

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Sunil K Mathanker, Paul R Weckler, and Randal K Taylor. "Canola - Weed Identification for Machine Vision based Patch Spraying." In 2008 Providence, Rhode Island, June 29 - July 2, 2008. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2008. http://dx.doi.org/10.13031/2013.24715.

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Pan, Jiazhi, Yueming Tang, and Yong He. "Research on crop and weed identification by NIR spectroscopy." In 27th International congress on High-Speed Photography and Photonics, edited by Xun Hou, Wei Zhao, and Baoli Yao. SPIE, 2007. http://dx.doi.org/10.1117/12.725951.

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Michael, J. Justina, and M. Thenmozhi. "Weed Identification and Removal: Deep Learning Techniques and Research Advancements." In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2022. http://dx.doi.org/10.1109/icirca54612.2022.9985603.

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Y Zhang, E S Staab, D C Slaughter, D K Giles, and D Downey. "Precision Automated Weed Control Using Hyperspectral Vision Identification and Heated Oil." In 2009 Reno, Nevada, June 21 - June 24, 2009. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2009. http://dx.doi.org/10.13031/2013.27119.

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Xianfeng Li and Zhong Chen. "Weed identification based on shape features and ant colony optimization algorithm." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620445.

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Reports on the topic "Weed identification"

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Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.
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Davis, Robert E., Edna Tanne, James P. Prince, and Meir Klein. Yellow Disease of Grapevines: Impact, Pathogen Molecular Detection and Identification, Epidemiology, and Potential for Control. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568792.bard.

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Grapevine yellows diseases characterized by similar symptoms have been reported in several countries including Israel, the United States, France, Italy, Spain, Germany and Australia. These diseases are among the most serious known in grapevine, but precise knowledge of the pathogens' identities and modes of their spread is needed to devise effective control stratgegies. The overall goals of this project were to develop improved molecular diagnostic procedures for detection and identification of the presumed mycoplasmalike organism (MLO) pathogens, now termed phytoplasmas, and to apply these procedures to investigate impact and spread and potential for controlling grapevine yellows diseases. In the course of this research project, increased incidence of grapevine yellows was found in Israel and the United States; the major grapevine yellows phytoplasma in Israel was identified and tis 16S rRNA gene characterized; leafhopper vectors of this grapevine yellows phytoplasma in Israel were identified; a second phytoplasma was discovered in diseased grapevines in Israel; the grapevine yellows disease in the U.S. was found to be distinct from that in Israel; grapevine yellows in Virginia, USA, was found to be caused by two different phytoplasmas; both phytoplasmas in Virginia grapevines were molecularly characterized and classified; commercial grapevines in Europe were discovered to host a phytoplasma associated with aster yellow disease in the USA, but this phytoplasma has not been found in grapevine in the USA; the Australian grapevine yellows phytoplasma was found to be distinct from the grapevine phytoplasmas in Israel, the United States and Europe and was described and named "Candidatus phytoplasma australiense", and weed host plants acting as potential reservoirs of the grapevine phytoplasmas were discovered. These and other findings from the project should aid in the design and development of strategies for managing the grapevine yellows disease problem.
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Priadko, Andrii O., Kateryna P. Osadcha, Vladyslav S. Kruhlyk, and Volodymyr A. Rakovych. Development of a chatbot for informing students of the schedule. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3744.

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The article describes the process of developing a chatbot to provide students with information about schedules using the Telegram mobile messenger. During the research, the following tasks have been performed: the analysis of notification systems for their use in the educational process, identification of problems of notifying students about the schedule (dynamic environment, traditional presentation of information, lack of round-the-clock access), substantiation of the choice of mobile technologies and Telegram messenger, determination of the requirements to the software, generalization of the chatbot functioning features, description of the structure, functionality of the program to get information about the schedule using a chatbot. The following tasks have been programmatically implemented: obtaining data from several pages of a spreadsheet (faculty / institute, red / green week, group number, day of the week, period number, discipline name, information about the teacher); presentation of data in a convenient form for the messenger (XML); implementation of the mechanism of convenient presentation of data in the messenger (chatbot). Using Python and the Telegram API, the software has been designed to increase students; immediacy in getting the information about the schedules, minimizing the time spent, and optimizing of planning of student activities and higher education institution functioning.
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Avis, William. Technical Aspects of e-Waste Management. Institute of Development Studies, March 2022. http://dx.doi.org/10.19088/k4d.2022.051.

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Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices, increased affordability and consumer appetite for new products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on the technical aspects e-waste value chains. The report should be read in conjunction with two earlier reports on e-waste management1. E-waste is any electrical or electronic equipment, including all components, subassemblies and consumables, which are part of the equipment at the time the equipment becomes waste. The exact treatment of Waste from Electrical and Electronic Equipment (WEEE) can vary enormously according to the category of WEEE and technology that is used. Electrical and electronic items contain a wide variety of materials. As a result of this complex mix of product types and materials, some of which are hazardous (including arsenic, cadmium, lead and mercury and certain flame retardants) multiple approaches to WEEE are required, each with specific technical guidelines. This report is structured as follows: Section two provides an introduction to the technical aspects of e-waste management, including a reflection on the challenges and complexities of managing a range of product types involving a range of components and pollutants. Section three provides an annotated bibliography of key readings that discuss elements of the technical aspects of managing e-waste. This bibliography includes readings on national guidelines, training manuals and technical notes produced by the Basel convention and courses. WEEE recycling can be a complex and multifaced process. In order to manage e-waste effectively, the following must be in place Legislative and regulatory frameworks Waste Prevention and minimisation guidelines Identification of waste mechanisms Sampling, analysis and monitoring expertise Handling, collection, packaging, labelling, transportation and storage guidelines Environmentally sound disposal guidelines Management is further complicated by the speed of technological advance with technologies becoming redundant much sooner than initially planned. Case studies show that the average actual lifetimes of certain electronic products are at least 2.3 years shorter than either their designed or desired lifetimes.
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Sessa, Guido, and Gregory Martin. A functional genomics approach to dissect resistance of tomato to bacterial spot disease. United States Department of Agriculture, January 2004. http://dx.doi.org/10.32747/2004.7695876.bard.

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The research problem. Bacterial spot disease in tomato is of great economic importance worldwide and it is particularly severe in warm and moist areas affecting yield and quality of tomato fruits. Causal agent of spot disease is the Gram-negative bacterium Xanthomonas campestris pv. vesicatoria (Xcv), which can be a contaminant on tomato seeds, or survive in plant debris and in association with certain weeds. Despite the economic significance of spot disease, plant protection against Xcvby cultural practices and chemical control have so far proven unsuccessful. In addition, breeding for resistance to bacterial spot in tomato has been undermined by the genetic complexity of the available sources of resistance and by the multiple races of the pathogen. Genetic resistance to specific Xcvraces have been identified in tomato lines that develop a hypersensitive response and additional defense responses upon bacterial challenge. Central goals of this research were: 1. To identify plant genes involved in signaling and defense responses that result in the onset of resistance. 2. To characterize molecular properties and mode of action of bacterial proteins, which function as avirulence or virulence factors during the interaction between Xcvand resistant or susceptible tomato plants, respectively. Our main achievements during this research program are in three major areas: 1. Identification of differentially expressed genes during the resistance response of tomato to Xcvrace T3. A combination of suppression subtractive hybridization and microarray analysis identified a large set of tomato genes that are induced or repressed during the response of resistant plants to avirulent XcvT3 bacteria. These genes were grouped in clusters based on coordinate expression kinetics, and classified into over 20 functional classes. Among them we identified genes that are directly modulated by expression of the type III effector protein AvrXv3 and genes that are induced also during the tomato resistance response to Pseudomonas syringae pv. tomato. 2. Characterization of molecular and biochemical properties of the tomato LeMPK3MAP kinase. A detailed molecular and biochemical analysis was performed for LeMPK3 MAP kinase, which was among the genes induced by XcvT3 in resistant tomato plants. LeMPK3 was induced at the mRNA level by different pathogens, elicitors, and wounding, but not by defense-related plant hormones. Moreover, an induction of LeMPK3 kinase activity was observed in resistant tomato plants upon Xcvinfection. LeMPK3 was biochemically defined as a dual-specificity MAP kinase, and extensively characterized in vitro in terms of kinase activity, sites and mechanism of autophosphorylation, divalent cation preference, Kₘand Vₘₐₓ values for ATP. 3. Characteriztion of molecular properties of the Xcveffector protein AvrRxv. The avirulence gene avrRxvis involved in the genetic interaction that determines tomato resistance to Xcvrace T1. We found that AvrRxv functions inside the plant cell, localizes to the cytoplasm, and is sufficient to confer avirulence to virulent Xcvstrains. In addition, we showed that the AvrRxv cysteine protease catalytic core is essential for host recognition. Finally, insights into cellular processes activated by AvrRxv expression in resistant plants were obtained by microarray analysis of 8,600 tomato genes. Scientific and agricultural significance: The findings of these activities depict a comprehensive and detailed picture of cellular processes taking place during the onset of tomato resistance to Xcv. In this research, a large pool of genes, which may be involved in the control and execution of plant defense responses, was identified and the stage is set for the dissection of signaling pathways specifically triggered by Xcv.
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THE CRACK DETECTION METHOD OF LONGITUDINAL RIB BUTT WELD OF STEEL BRIDGE BASED ON ULTRASONIC LAMB WAVE. The Hong Kong Institute of Steel Construction, August 2022. http://dx.doi.org/10.18057/icass2020.p.265.

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Ultrasonic Lamb wave is an efficient and fast new nondestructive testing technology. Due to its characteristics of wide detection range, high efficiency and strong defect identification ability, ultrasonic Lamb wave has developed rapidly in the field of nondestructive testing in recent years. Meanwhile, the longitudinal rib butt weld is one of the most important failure modes of steel bridge deck, which seriously endangers the safety and durability of long-span steel bridges. In this paper, the fatigue failure mode of longitudinal rib butt weld is considered, the propagation process of ultrasonic guided wave in longitudinal rib butt weld is studied by using finite element real-time simulation, and the influences of different weld and crack parameters on ultrasonic guided wave is analyzed. The results show that the method based on ultrasonic Lamb wave has good applicability to crack detection of longitudinal rib butt weld of steel bridge deck. And this method provides a new idea and method for steel structure damage detection and monitoring.
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Safeguarding through science: Center for Plant Health Science and Technology 2009 Accomplishments. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, February 2011. http://dx.doi.org/10.32747/2011.7296843.aphis.

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The Center for Plant Health Science and Technology (CPHST) provides scientific support for the regulatory decisions and operations of the Animal and Plant Health Inspection Service’s (APHIS) Plant Protection and Quarantine (PPQ) program in order to safeguard U.S. agriculture and natural resources. CPHST is responsible for ensuring that PPQ has the information, tools, and technology to make the most scientifically valid regulatory and policy decisions possible. In addition, CPHST ensures that PPQ’s operations have the most scientifically viable and practical tools for pest exclusion, detection, and management. This 2009 CPHST Annual Report is intended to offer an in-depth look at the status of our programs and the progress CPHST has made toward the Center’s long-term strategic goals. CPHST's work is organized into six National Science Programs: Agricultural Quarantine Inspection and Port Technology; Risk and Pathway Analysis; Domestic Surveillance, Detection, and Identification; Emergency Response; Response and Recovery Systems Technology - Arthropods; and Response and Recovery Systems Technology - Plant Pathogens and Weeds. the scientists of CPHST provide leadership and expertise in a wide range of fields, including risk assessments that support trade, commodity quarantine treatments, pest survey and detection methods, molecular diagnostics, biological control techniques, integrated pest management, and mass rearing of insects. Some highlights of significant CPHST efforts in 2009 include: Establishment of the National Ornamentals Research Site at Dominican University of California, Established LBAM Integrated Pest Management and Survey Methods, Continue to develop Citrus Greening/Huanglongbing Management Tools, and further European Grapevine Moth (EGVM) Response.
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Center for Plant Health Science and Technology Accomplishments, 2007. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, December 2008. http://dx.doi.org/10.32747/2008.7296841.aphis.

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This past year’s hard work and significant changes have enabled CPHST—a division of the U.S. Department of Agriculture (USDA), APHIS Plant Protection and Quarantine (PPQ) program—to be an organization more capable and better aligned to support and focus on PPQ’s scientific needs. In 2007, CPHST developed the first PPQ strategic plan for CPHST. The plan shows where CPHST is going over the next 5 years, how it is going to get there, and how it will know if it got there or not. Moreover, CPHST plan identifies critical elements of PPQ’s overall strategic plan that must be supported by the science and technology services CPHST provides. The strategic plan was followed by an operational plan, which guarantees that the strategic plan is a living and breathing document. The operational plan identifies the responsibilities and resources needed to accomplish priorities in this fiscal year and measures our progress. CPHST identifies the pathways by which invasive plant pests and weeds can be introduced into the United States. CPHST develops, adapts, and supports technology to detect, identify, and mitigate the impact of invasive organisms. CPHST helps to ensure that the methods, protocols, and equipment used by PPQ field personnel are effective and efficient. All the work of CPHST is identified under one of the five program areas: Agricultural Quarantine Inspection and Port Technology, Molecular Diagnostics and Biotechnology, Response and Recovery Systems Technology, Risk and Pathway Analysis, and Survey Detection and Identification. CPHST scientists are leaders in various fields, including risk assessment, survey and detection, geographic information systems (GIS), molecular diagnostics, biocontrol techniques, methods and treatment, and mass rearing of insects. The following list outlines some of CPHST’s efforts in 2007: Responding to Emergencies, Developing and Supporting Technology for Treatments, Increasing Diagnostic Capacity, and Supporting Trade.
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