Dissertations / Theses: 'Agricultural processing plants'

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Wolters, Dustin Joseph. "Assessment of Corn Plant Population at Emergence from Processed Color Aerial Imagery." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437666741.

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Moreira, Lucas Pladevall. "Proposta de sensor de amido em folha de plantas." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-05122017-140650/.

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Para contribuir com o desenvolvimento da automação agrícola, este trabalho descreve abordagens metodológicas e cria uma proposta para a elaboração de um sensor de medição de amido em folhas de plantas, para a quantificação de amido nas folhas das plantas, utilizando técnicas de processamento de imagem em cortes de secção transversal de folhas de goiabeira Psidium guajava L marcadas com Lugol para quantificar após calibração os grãos de amido presentes na folha. Os resultados obtidos foram satisfatórios, com redução do tempo necessário para obtenção da medição, propiciando sua utilização como sensor em sistemas de controle de crescimento de plantas em malha fechada, em especial aqueles que utilizam iluminação artificial.
To contribute to the development of agricultural automation, this work describes methodological approaches and creates a proposal for the elaboration of a starch measurement sensor in plant leaves, for the quantification of starch in the leaves of the plants, using image processing techniques in cross-sections of Psidium guajava L guava leaves marked with Lugol to quantify after calibration the starch grains present in the leaf. The results obtained were satisfactory, with a reduction in the time required to obtain the measurement, favoring its use as a sensor in closed-loop plant growth control systems, especially those using artificial lighting.

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Kusmak, Michael T. "An analysis of the economic feasibility of a pistachio processing facility." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/618.

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Ma, Xing. "Characterization and Management of Bacterial Leaf Spot of Processing Tomato in Ohio." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440386548.

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Kacira, Murat. "Non-contact and early detection of plant water stress using infrared thermometry and image processing /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665236214.

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Rethwisch, Michael D., Dick Beckstead, and Larry Parker. "Effect of a Plant Growth Regulator on Green Beans Grown for Processing." College of Agriculture, University of Arizona (Tucson, AZ), 1996. http://hdl.handle.net/10150/214770.

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Three rates of the plant growth regulator Foliar Triggrr were applied to green beans grown for processing at 5% bloom. The 6 oz rate increased yields of size 1 and 2 beans compared to all other treatments and the untreated check and had the fewest size 3 beans (which would be culls). The 11 oz rate was similar to the untreated check while the 16 oz rate decreased yields. Total bean numbers per plant were similar. Although treatment differences in this experiment were not statistically significant, a yield increase of 10.2% for the 6 oz rate compared with the untreated check may well result in increased economic returns.

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Cacho, Joyce Agnes Sabina. "Growth in Brazil's soybean processing industry and government policies, 1970-1993 /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9962507.

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Calixte, Sophie. "RNA processing of the ccmFn-rps1 and rpl5-Psirps14-cox3 loci in wheat mitochondria during seedling development." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27580.

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Plant mitochondria possess a gene expression system in which post-transcriptional events, such as transcript end maturation and turnover mechanisms play a key role in regulating the transcriptome. In addition, during early developmental stages of embryo germination, differing transcript profiles have been seen. This research focuses on two loci in wheat mitochondria, ccmFn-rps1 and rpl5-Psirps14-cox3, to elucidate the transcription and post-transcriptional events involved in their expression. Northern analysis of the ccmFN-rps1 genes during early seed-to-seedling development reveals a 3.2 kb primary transcript and a 2.7 kb bicistronic mRNA. A 0.7 kb monocistronic rps1 mRNA is detectable up to 2d but there is no detectable monocistronic ccmFN transcript during the stages examined. Transcript ends were mapped using circular-RT-PCR and phosphatase treatment at three different developmental stages and revealed two processing sites as well as a single 3' end common to all three transcripts. The 5' ends of the processed rps1 transcripts are heterogeneous and do not always include the start codon, questioning the rps1 transcript functionality. Gene order varies between plant species due to the high recombination rate in mitochondrial genomes, as is seen for rpl5-Psirps14 in wheat and rice. In both plants, the functional rps14 gene is encoded in the nucleus and the mitochondrial rps14 copy is a pseudogene. In wheat, rpl5-Psirps14 are co-transcribed with cox3 as two RNA species of 3.5 kb and 2.7 kb at 24hr post-imbibition and exhibit developmentally-specific differences in abundance in seedlings. Two promoter regions were mapped in wheat upstream of rpl5 and both transcripts have the same 3' end. In rice 24hr and 6d however, rpl5-Psirps14 are co-transcribed as a 1.4 kb bicistronic mRNA. This presumably reflects the different regulatory signals used in different species. In addition, rpl5 has been subject to several independent gene transfers to the nucleus in the cereal lineages. For example, there is a functional copy of rpl5 in the mitochondria and the nucleus in wheat but it is absent from the mitochondria in rye and maize. In oat mitochondria, rpl5 appears to be a pseudogene and in barley, rearrangements at the 3' end and low transcript levels question its functionality. The characterization of transcription initiation sites, processing sites and 3' ends for these two loci reflect the relaxed nature and flexibility of signals exploited by plant mitochondria. This research supports the significant role of post-transcriptional events in the regulation of gene expression in plant mitochondria.

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Rethwisch, Michael D., Charles Poole, Rick Poole, and Rudy Pacheco. "Effect of Dry Seed+ Application at Planting 1998 on Processing Onion Yields." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2002. http://hdl.handle.net/10150/214952.

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Dry Seed+, a fertilizer/plant growth regulator, was applied to processing onions at planting in the fall of 1998 to evaluate the effect on yields. Yield data obtained June 1999 indicated no effect in this experiment, although this was dissimilar to other trial results. Salt content at the field was thought to have limited yields, and have stressed growing plants, which may have contributed to lack of yield differences noted. Further testing is necessary to further evaluate this product.

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Brilhador, Anderson. "Análise semi-automática do arranjo espacial de plantas de milho utilizando visão computacional." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/2954.

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A demanda mundial por alimentos cresce a cada ano, tornando necessário o desenvolvimento de novas tecnologias que aumentem a produção de grãos sem aumentar as áreas destinadas ao para plantio. A cultura de milho é uma das principais commodities do mundo, sendo utilizada na alimentação humana e como ração de outros animais, além de possuir outros fins industriais. O milho é sensível ao arranjo espacial de plantas e qualquer variação no padrão de distribuição pode levar à redução na produção do milho. Atualmente, o processo de verificação da uniformidade dos espaçamentos entre plantas é realizado de forma manual por agrônomos e produtores, a fim de prever possíveis perdas de produção. Nesse contexto, este trabalho propõe uma abordagem automática para a análise do arranjo espacial de plantas por meio da mensuração dos espaçamentos entre plantas de milho em fases inicias de crescimento. A partir dessa mensuração são extraídas informações relevantes como densidade populacional, uniformidade do plantio e estimativas de perdas. A abordagem proposta utiliza técnicas de visão computacional de baixo custo computacional para identificar as plantas de milho e mensurar os espaçamentos entre plantas, permitindo seu uso em dispositivos com baixo poder computacional como smartphones e tablets. Um conjunto de imagens foi construído como uma contribuição adicional do trabalho contento 222 imagens panorâmicas da linha de plantio de milho em três condições de plantio: direto, convencional e direto após aplicação de herbicidas. Os resultados dos experimentos alcançaram uma taxa de 90% de precisão e 87% de sensibilidade na identificação das plantas de milhos presentes na base. Uma comparação entre as medidas dos espaçamentos entre plantas realizadas de forma manual e por visão computacional, não apresentou diferenças significativas entre as medições, indicando a eficácia da abordagem proposta no trabalho.
Global demand for food is growing every year, requiring the development of new technologies that increase grain production without increasing the areas destined for planting. The corn crop is a major commodity in the world and is used as food, feed for other animals, in addition to having other industrial purposes. Corn is sensitive to the spatial arrangement of plants and any variation in distribution pattern can lead to reduction in the production of corn. Currently, the process of checking the uniformity of spacing between plants is done manually by agronomists and producers in order to predict possible production losses. In this context, this paper proposes an automatic approach to the analysis of the spatial arrangement of plants by measuring the spacing between corn plants in early stages of growth. From this measurement are extracted relevant information such as population density, uniformity of planting and loss estimates. The proposed approach uses computer vision techniques of low computational cost to identify corn plants and measure the spacing between plants, allowing its use in devices with low computational power such as smartphones and tablets. A set of images was built as an additional contribution of work, containing 222 panoramic images of corn planting in three conditions of planting: direct, conventional and direct after applying herbicides. The experimental results achieve 90% of rate accuracy and 87% sensitivity in identification of corn plants present on the base. A comparison of the measurements of the distances between plants made of manual and computer vision way, no presented significant differences between the measurements, indicating the effectiveness of the proposed approach at work.

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Rethwisch, Michael D. "2001 Yield Response of Processing Onions in the Palo Verde Valley Treated with AuxiGro® WP." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2002. http://hdl.handle.net/10150/214951.

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Processing onions were treated with AuxiGro in spring 2001. Applications were applied at four and 7.5 weeks prior to harvest. A single application of 4 oz. product/acre at either four or 7.5 weeks prior to harvest increased yield by approximately 0.7 tons/acre. Two applications resulted in a 1.3 tons/acre yield increase. Differences noted were not statistically different, but the two application treatment provided an increased net return of approximately $95/acre.

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Perissini, Ivan Carlos. "Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-25052018-095947/.

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O uso da visão computacional vem ganhando espaço no contexto agrícola, especialmente com a evolução do conceito da agricultura de precisão. Aplicações como irrigação, fertilização e controle de pragas são apenas alguns dos cenários que essa tecnologia pode atender. Entretanto, a demanda por sistemas acessíveis e eficientes aliada às inconstâncias e ruídos visuais de um ambiente externo, apresentam desafios a estes processos. Foi proposto neste trabalho uma análise da literatura e uma série de investidas experimentais de técnicas de processamento de imagens, para buscar melhores relações entre custo computacional e desempenho da detecção de mudas de plantas, visando atingir operações em tempo real com o uso de hardwares comuns e de baixo custo. Para tanto o trabalho investiga a composição de estratégias de segmentação a partir de diferentes espaços de cor e métodos de constância de cor, de forma a reduzir a variação luminosa, uma das maiores fontes de instabilidade nas aplicações de visão na agricultura. Os experimentos propostos foram divididos em duas fases; na primeira o sistema de medidas foi avaliado, definindo as métricas e condições experimentais adequadas para a segunda fase, composta de uma sequência de experimentos comparativos entre estratégias de segmentação sob diferentes condições de iluminação. Os resultados mostraram que as soluções são muito dependentes das condições da cena e uma série de alternativas promissoras de segmentação foram obtidas. Sua elegibilidade, porém, depende de considerações sobre a disponibilidade computacional e contexto de aplicação.
The use of computer vision has been gaining ground in the agricultural context, especially with the evolution of the concept of precision agriculture. Applications such as irrigation, fertilization and pest control are just some of the scenarios that this technology can be used. However, the demand for accessible and efficient systems together with the variations and visual noise from an external environment presents challenges to these processes. It was proposed in this study an analysis of the literature and a series of experimental investigations of image processing techniques, to search for better relations between computational cost and performance in the detection of seedlings, aiming to achieve real time operations with the use of common and low cost hardware. For this, the work investigates the composition of segmentation strategies from different color spaces and color constancy methods, in order to combat light variation, one of the major sources of instability in agricultural vision applications. The proposed experiments were divided into two phases; in the first the measurement system was evaluated, defining the metrics and suitable conditions for the experiments at second phase, composed of a sequence of comparative experiments of segmentation strategies under different lighting conditions. The results showed that the solutions are very dependent on the conditions of the scene and a series of promising segmentation alternatives were obtained. Their eligibility, however, depends on considerations about the computational availability and context of the application.

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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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Santos, Ana Paula de Oliveira. "Desenvolvimento de descritores de imagens para reconhecimento de padrões de plantas invasoras (folhas largas e folhas estreitas)." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/416.

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Universidade Federal de Sao Carlos
In Brazil, the development of tools for weeds recognition, capable of aiding risk detection and decision making on the fieldwork is still embryonic. This master s thesis presents the development of a pattern recognition system that recognizes weeds and gives the occupation percentage of wide and narrow leaves in an agricultural production system, with digital image processing techniques. The development was based on considerations about image acquisition, pre-processing, texture based segmentation, descriptors for weeds recognition and occupation percentage of each kind of leaf. The validation has been developed considering geometric patterns generated in laboratory, as well as others obtained of a maize (Zea mays) production agricultural environment, i. e. two species of weeds, one with wide leaves (Euphorbia heterophylla L.) and other with narrow leaves (Digitaria sanguinalis Scop.). The results show recognition of about 84.24 percent for wide leaves and 80.17 percent for narrow leaves in agricultural environment and also the capability to spot weed on unreachable locations by natural vision. Besides, the method presents application in precision agriculture to improve the decision making in pulverization processes.
No Brasil é ainda embrionário o desenvolvimento de ferramentas de reconhecimento de plantas invasoras, capazes de auxiliar a tomada de decisão e indicar o seu risco no sistema de produção. Este trabalho apresenta o desenvolvimento de um sistema de reconhecimento de padrões de plantas invasoras e percentuais de ocupação de folhas largas e folhas estreitas, em sistemas de produção agrícola, utilizando técnicas de processamento digital de imagens. Para o desenvolvimento houve a consideração das etapas de aquisição das imagens, pré-processamento, segmentação baseada em textura, descritores para o reconhecimento das plantas invasoras e percentual de ocupação de cada tipo de planta. A validação foi desenvolvida considerando padrões geométricos gerados em laboratório, bem como o próprio ambiente de produção agrícola de milho (Zea mays), tomando por base duas espécies de plantas invasoras, sendo uma de folha larga (Euphorbia heterophylla L.), e outra de folha estreita (Digitaria sanguinalis Scop.). Resultados indicam uma taxa de acerto no reconhecimento em ambiente de campo da ordem de 84,24% para folhas largas e da ordem de 80,17% para folhas estreitas, além da capacidade de identificar plantas invasoras em locais restritos a visão natural. Adicionalmente, o resultado obtido apresenta potencial para a aplicação no manejo baseado em agricultura de precisão, o que auxilia na tomada de decisão em pulverização agrícola.

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Ramalingam, Nagarajan. "Non-contact multispectral and thermal sensing techniques for detecting leaf surface wetness." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1104392582.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xxii, 271 p.; also includes graphics (some col.) Includes bibliographical references (p. 206-214).

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Caldas, Júnior Carlos Roberto Dutra [UNESP]. "Implementação em hardware de um sistema inteligente para detecção de plantas daninhas em plantações de soja utilizando máquinas de vetores de suporte e redes neurais artificiais." Universidade Estadual Paulista (UNESP), 2012. http://hdl.handle.net/11449/98648.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
A presença de sistemas automatizados é cada vez mais comum para as pessoas. Seus exemplos vão desde máquinas de lavar, que executam praticamente todo o processo de lavagem e secagem de roupas, até linhas de produção em fábricas dos mais diversos produtos. Esses são exemplos de aplicações que exigem pouca interferência humana no processo, já que as etapas realizadas pelos sistemas são bem definidas e iterativas. Porém, outros tipos de processos podem exigir capacidade de discernimento daquele – ou daquilo – que os executam. Para automatizar esse tipo de processo uma das alternativas é o uso de técnicas de inteligência artificial. Esse trabalho visa realizar uma análise comparativa entre técnicas de inteligência artificial, quais sejam Redes Neurais Artificiais e Máquinas de Vetores de Suporte. Com essa análise espera-se estabelecer qual técnica é mais vantajosa para implementação em hardware de sistemas inteligentes, por meio do uso das principais métricas de projeto de circuitos digitais: tamanho do circuito gerado, consumo de energia e desempenho. Para tanto, foram realizados diversos testes com técnicas de pré-processamento e extração de características das imagens para determinar requisitos necessários para o funcionamento do sistema. A partir desses requisitos foram implementadas diversas arquiteturas de sistemas inteligentes para obter-se o classificador mais adequado para resolver o problema. Por fim, o classificador escolhido foi implementado em FPGA na forma de um módulo, o qual se integrará a um sistema maior, para interpretação de imagens digitais para detecção de ervas daninhas em plantações de soja
Automated systems have become common for people. Examples range from washing machines, which perform almost the entire cloth washing and drying process, to the production of many products. These are examples of applications that require modest human interference, since the steps taken by the systems are well defined and iterative. However, other processes may require a capacity of judgment of the natural or artificial system performing them. An alternative to automate this kind of process is the use of artificial intelligence techniques. This study aims at a comparative analysis of artificial intelligence techniques, namely Artificial Neural Networks and Support Vector Machines. With this analysis we hope to establish which technique is more advantageous for hardware implementation of an intelligent system, through the use of key metrics for digital circuit design: circuit size, power consumption and performance. Therefore, several tests were performed with image preprocessing and feature extraction techniques to determine requirements for system operation. From these requirements, various architectures for intelligent systems were implemented to obtain the most appropriate classifier to solve the problem. Finally, the chosen classifier was implemented in FPGA as a module to fit into a larger system for digital image interpretation for the detection of weeds in crops of soybeans

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Caldas, Júnior Carlos Roberto Dutra. "Implementação em hardware de um sistema inteligente para detecção de plantas daninhas em plantações de soja utilizando máquinas de vetores de suporte e redes neurais artificiais /." São José do Rio Preto : [s.n.], 2012. http://hdl.handle.net/11449/98648.

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Orientador: Norian Marranghello
Banca: Adilson Gonzaga
Banca: Rodrigo Capobianco Guido
Resumo: A presença de sistemas automatizados é cada vez mais comum para as pessoas. Seus exemplos vão desde máquinas de lavar, que executam praticamente todo o processo de lavagem e secagem de roupas, até linhas de produção em fábricas dos mais diversos produtos. Esses são exemplos de aplicações que exigem pouca interferência humana no processo, já que as etapas realizadas pelos sistemas são bem definidas e iterativas. Porém, outros tipos de processos podem exigir capacidade de discernimento daquele - ou daquilo - que os executam. Para automatizar esse tipo de processo uma das alternativas é o uso de técnicas de inteligência artificial. Esse trabalho visa realizar uma análise comparativa entre técnicas de inteligência artificial, quais sejam Redes Neurais Artificiais e Máquinas de Vetores de Suporte. Com essa análise espera-se estabelecer qual técnica é mais vantajosa para implementação em hardware de sistemas inteligentes, por meio do uso das principais métricas de projeto de circuitos digitais: tamanho do circuito gerado, consumo de energia e desempenho. Para tanto, foram realizados diversos testes com técnicas de pré-processamento e extração de características das imagens para determinar requisitos necessários para o funcionamento do sistema. A partir desses requisitos foram implementadas diversas arquiteturas de sistemas inteligentes para obter-se o classificador mais adequado para resolver o problema. Por fim, o classificador escolhido foi implementado em FPGA na forma de um módulo, o qual se integrará a um sistema maior, para interpretação de imagens digitais para detecção de ervas daninhas em plantações de soja
Abstract: Automated systems have become common for people. Examples range from washing machines, which perform almost the entire cloth washing and drying process, to the production of many products. These are examples of applications that require modest human interference, since the steps taken by the systems are well defined and iterative. However, other processes may require a capacity of judgment of the natural or artificial system performing them. An alternative to automate this kind of process is the use of artificial intelligence techniques. This study aims at a comparative analysis of artificial intelligence techniques, namely Artificial Neural Networks and Support Vector Machines. With this analysis we hope to establish which technique is more advantageous for hardware implementation of an intelligent system, through the use of key metrics for digital circuit design: circuit size, power consumption and performance. Therefore, several tests were performed with image preprocessing and feature extraction techniques to determine requirements for system operation. From these requirements, various architectures for intelligent systems were implemented to obtain the most appropriate classifier to solve the problem. Finally, the chosen classifier was implemented in FPGA as a module to fit into a larger system for digital image interpretation for the detection of weeds in crops of soybeans
Mestre

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Viljanen-Rollinson, S. L. H. "Expression and detection of quantitative resistance to Erysiphe pisi DC. in pea (Pisum sativum L.)." Lincoln University, 1996. http://hdl.handle.net/10182/1657.

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Characteristics of quantitative resistance in pea (Pisum sativum L.) to Erysiphe pisi DC, the pathogen causing powdery mildew, were investigated. Cultivars and seedlines of pea expressing quantitative resistance to E. pisi were identified and evaluated, by measuring the amounts of pathogen present on plant surfaces in field and glasshouse experiments. Disease severity on cv. Quantum was intermediate when compared with that on cv. Bolero (susceptible) and cv. Resal (resistant) in a field experiment. In glasshouse experiments, two groups of cultivars, one with a high degree of resistance and the other with nil to low degrees of resistance to E. pisi, were identified. This indicated either that a different mechanism of resistance applied in the two groups, or that there has been no previous selection for intermediate resistance. Several other cultivars expressing quantitative resistance were identified in a field experiment. Quantitative resistance in Quantum did not affect germination of E. pisi conidia, but reduced infection efficiency of conidia on this cultivar compared with cv. Pania (susceptible). Other epidemiological characteristics of quantitative resistance expression in Quantum relative to Pania were a 33% reduction in total conidium production and a 16% increase in time to maximum daily conidium production, both expressed on a colony area basis. In Bolero, the total conidium production was reduced relative to Pania, but the time to maximum spore production on a colony area basis was shorter. There were no differences between the cultivars in pathogen colony size or numbers of haustoria produced by the pathogen. Electron microscope studies suggested that haustoria in Quantum plants were smaller and less lobed than those in Pania plants and the surface area to volume ratios of the lobes and haustorial bodies were larger in Pania than in Quantum. The progress in time and spread in space of E. pisi was measured in field plots of cultivars Quantum, Pania and Bolero as disease severity (proportion of leaf area infected). Division of leaves (nodes) into three different age groups (young, medium, old) was necessary because of large variability in disease severity within plants. Disease severity on leaves at young nodes was less than 4% until the final assessment at 35 days after inoculation (dai). Exponential disease progress curves were fitted for leaves at medium nodes. Mean disease severity on medium nodes 12 dai was greatest (P<0.001) on Bolero and Pania (9.3 and 6.8% of leaf area infected respectively), and least on Quantum (1.6%). The mean disease relative growth rate was greatest (P<0.001) for Quantum, but was delayed compared to Pania and Bolero. Gompertz growth curves were fitted to disease progress data for leaves at old nodes. The asymptote was 78.2% of leaf area infected on Quantum, significantly lower (P<0.001) than on Bolero or Pania, which reached 100%. The point of inflection on Quantum occurred 22.8 dai, later (P<0.001) than on Pania (18.8 dai) and Bolero (18.3 dai), and the mean disease severity at the point of inflection was 28.8% for Quantum, less (P<0.00l) than on Pania (38.9%) or Bolero (38.5%). The average daily rates of increase in disease severity did not differ between the cultivars. Disease progress on Quantum was delayed compared with Pania and Bolero. Disease gradients from inoculum foci to 12 m were detected at early stages of the epidemic but the effects of background inoculum and the rate of disease progress were greater than the focus effect. Gradients flattened with time as the disease epidemic intensified, which was evident from the large isopathic rates (between 2.2 and 4.0 m d⁻¹) Some epidemiological variables expressed in controlled environments (low infection efficiency, low maximum daily spore production and long time to maximum spore production) that characterised quantitative resistance in Quantum were correlated with disease progress and spread in the field. These findings could be utilised in pea breeding programmes to identify parent lines from which quantitatively resistant progeny could be selected.

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Peacock, Lora. "Eco-climatic assessment of the potential establishment of exotic insects in New Zealand." Lincoln University, 2005. http://hdl.handle.net/10182/1530.

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To refine our knowledge and to adequately test hypotheses concerning theoretical and applied aspects of invasion biology, successful and unsuccessful invaders should be compared. This study investigated insect establishment patterns by comparing the climatic preferences and biological attributes of two groups of polyphagous insect species that are constantly intercepted at New Zealand's border. One group of species is established in New Zealand (n = 15), the other group comprised species that are not established (n = 21). In the present study the two groups were considered to represent successful and unsuccessful invaders. To provide background for interpretation of results of the comparative analysis, global areas that are climatically analogous to sites in New Zealand were identified by an eco-climatic assessment model, CLIMEX, to determine possible sources of insect pest invasion. It was found that south east Australia is one of the regions that are climatically very similar to New Zealand. Furthermore, New Zealand shares 90% of its insect pest species with that region. South east Australia has close trade and tourism links with New Zealand and because of its proximity a new incursion in that analogous climate should alert biosecurity authorities in New Zealand. Other regions in western Europe and the east coast of the United States are also climatically similar and share a high proportion of pest species with New Zealand. Principal component analysis was used to investigate patterns in insect global distributions of the two groups of species in relation to climate. Climate variables were reduced to temperature and moisture based principal components defining four climate regions, that were identified in the present study as, warm/dry, warm/wet, cool/dry and cool/moist. Most of the insect species established in New Zealand had a wide distribution in all four climate regions defined by the principal components and their global distributions overlapped into the cool/moist, temperate climate where all the New Zealand sites belong. The insect species that have not established in New Zealand had narrow distributions within the warm/wet, tropical climates. Discriminant analysis was then used to identify which climate variables best discriminate between species presence/absence at a site in relation to climate. The discriminant analysis classified the presence and absence of most insect species significantly better than chance. Late spring and early summer temperatures correctly classified a high proportion of sites where many insect species were present. Soil moisture and winter rainfall were less effective discriminating the presence of the insect species studied here. Biological attributes were compared between the two groups of species. It was found that the species established in New Zealand had a significantly wider host plant range than species that have not established. The lower developmental threshold temperature was on average, 4°C lower for established species compared with non-established species. These data suggest that species that establish well in New Zealand have a wide host range and can tolerate lower temperatures compared with those that have not established. No firm conclusions could be drawn about the importance of propagule pressure, body size, fecundity or phylogeny for successful establishment because data availability constrained sample sizes and the data were highly variable. The predictive capacity of a new tool that has potential for eco-climatic assessment, the artificial neural network (ANN), was compared with other well used models. Using climate variables as predictors, artificial neural network predictions were compared with binary logistic regression and CLIMEX. Using bootstrapping, artificial neural networks predicted insect presence and absence significantly better than the binary logistic regression model. When model prediction success was assessed by the kappa statistic there were also significant differences in prediction performance between the two groups of study insects. For established species, the models were able to provide predictions that were in moderate agreement with the observed data. For non-established species, model predictions were on average only slightly better than chance. The predictions of CLIMEX and artificial neural networks when given novel data, were difficult to compare because both models have different theoretical bases and different climate databases. However, it is clear that both models have potential to give insights into invasive species distributions. Finally the results of the studies in this thesis were drawn together to provide a framework for a prototype pest risk assessment decision support system. Future research is needed to refine the analyses and models that are the components of this system.

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WEN, YAO HUI, and 温耀輝. "Planning and Evaluation of Primary Processing Plant for Organic Agricultural Products." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/42hcpv.

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碩士
台北海洋科技大學
食品科技與行銷系碩士班
106
"Organic Agriculture" refers to observing the principle of the sustainable use of natural resources, not using synthetic chemical substances, emphasizing the management system of conservation of water and land resources and ecological balance, and achieving the agriculture that produces natural and safe products. Nowadays, under the influence of government policies and food safety incidents, consumers' consumption patterns tend to be diversified and healthy, and more attention is paid to healthy diets such as organic food and healthy food, which in turn drives the vigorous development of the organic agricultural products market. At present, the processing methods of organic agricultural products are mostly primary or self-produced. The processing environment is relatively simple, and the food sanitation and safety are worrying. This study collects and sorts the literature of organic agriculture and the planning of food factories. We use the Heuristic Structure to organize and analyze the information, use the Delphi method to correct and evaluate the data, and hope to establish a goal for factories’ food sanitation and safety. The results show that experts and scholars are more focusing on whether the working areas configuration follows the organic verification standard, the working process is smooth enough, and the personnel and materials’ traffic flow complies with the food sanitation and safety. As for the degree of achievement, after using Delphi method to view and adjust, the achievement of the work area is 3.7, the achievement of the work process is 3.4, and the achievement of the factory operation activity is 3.5. The degree has been significantly improved after using Delphi Method twice to correct.

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Johnstone, Paul R. "Nutrition and irrigation studies with processing tomato (Lycopersicon esculentum Mill.) : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Plant Science at Massey University, Palmerston North, New Zealand." 2005. http://hdl.handle.net/10179/1603.

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Content removed due to copyright restriction: Appendix IV. Managing fruit soluble solids with late-season deficit irrigation in drip-irrigated processing tomato production (HortScience 40: 1 857-1861 ).
Improved fertilizer and irrigation management has become increasingly important for tomatoes (Lycopersicon esculentum Mill.) grown for processing. To reduce potential nutrient loss to the environment due to excessive supply, fertilizer recommendations should reflect plant demand determined in an optimal root environment. An aeroponics experiment examined the effect of low and high nutrient supply during vegetative growth, fruit development and fruit ripening. The use of aeroponics in a glasshouse environment allowed control of fertility directly at the root surface. A further experiment applying aeroponics results was established in the field using drip-fertigation. Both studies were conducted at Massey University, Palmerston North. Across experiments, fruit yield was largely determined by vegetative growth in the 6-8 weeks after transplanting; high fruit yields (> 90 Mg ha-1) were associated with improved vegetative growth, and in particular larger leaf area. Mild N deficiency was the principal cause of poor vegetative growth in low nutrient supply treatments. Higher yield resulted from greater fruit number. Reinstating adequate fertility after vegetative growth stopped and fruit number was determined did not increase fruit yield. For maximum fruit yield, plant uptake of N and K was 9.4 and 13.8 g plant-1, respectively (equivalent to approximately 210 and 310 kg ha-1 at a medium planting density). Greatest nutrient uptake occurred during fruit development. Where practical, fertilizer application should be concentrated during fruit growth. Heavy late-season K fertigation did not increase the soluble solids concentration (SSC) of fruit. Although offering considerable flexibility in nutrient fertigation, the use of drip irrigation often results in undesirably low SSC. Late-season irrigation management strategies to increase fruit SSC without excessive yield loss were subsequently investigated in drip-irrigated fields. Two experiments were conducted at the University of California, Davis. Irrigation cutoff prior to fruit ripening reduced fruit set, decreased fruit size, and increased the incidence of fruit rots, making this approach uneconomical. Irrigation cutback to 25-50% of reference evapotranspiration imposed at the onset of fruit ripening (approximately 6 weeks preharvest) was sufficient to improve fruit SSC and maintain Brix yields (Mg Brix solids ha-1) compared to the current grower practice (late cutoff). Irrigation cutbacks imposed during ripening did not cause excessive canopy dieback, nor were fruit culls or rots increased when the crop was harvested at commercial maturity. Fruit colour and pH were not adversely affected by irrigation cutback. Brix monitoring of the earliest ripening fruit (when 30-60 % of the fruit surface shows a colour other than green) can help classify fields as to the severity of irrigation cutback required to reach desirable fruit SSC at harvest. Combined, these techniques offer considerable flexibility in managing fields for improved fruit SSC levels.

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Ratzinger, Astrid. "Development and application of LC-MS-based differential metabolic profiling in plant systems." Doctoral thesis, 2008. http://hdl.handle.net/11858/00-1735-0000-0006-B024-7.

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23

(9224231), Dongdong Ma. "Ameliorating Environmental Effects on Hyperspectral Images for Improved Phenotyping in Greenhouse and Field Conditions." Thesis, 2020.

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Hyperspectral imaging has become one of the most popular technologies in plant phenotyping because it can efficiently and accurately predict numerous plant physiological features such as plant biomass, leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems have been deployed in both greenhouse and field phenotyping activities. However, the hyperspectral imaging quality is severely affected by the continuously changing environmental conditions such as cloud cover, temperature and wind speed that induce noise in plant spectral data. Eliminating these environmental effects to improve imaging quality is critically important. In this thesis, two approaches were taken to address the imaging noise issue in greenhouse and field separately. First, a computational simulation model was built to simulate the greenhouse microclimate changes (such as the temperature and radiation distributions) through a 24-hour cycle in a research greenhouse. The simulated results were used to optimize the movement of an automated conveyor in the greenhouse: the plants were shuffled with the conveyor system with optimized frequency and distance to provide uniform growing conditions such as temperature and lighting intensity for each individual plant. The results showed the variance of the plants’ phenotyping feature measurements decreased significantly (i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly, the environmental effects (i.e., sun radiation) on aerial hyperspectral images in field plant phenotyping were investigated and modeled. An artificial neural network (ANN) method was proposed to model the relationship between the image variation and environmental changes. Before the 2019 field test, a gantry system was designed and constructed to repeatedly collect time-series hyperspectral images with 2.5 minutes intervals of the corn plants under varying environmental conditions, which included sun radiation, solar zenith angle, diurnal time, humidity, temperature and wind speed. Over 8,000 hyperspectral images of corn (Zea mays L.) were collected with synchronized environmental data throughout the 2019 growing season. The models trained with the proposed ANN method were able to accurately predict the variations in imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus, the ANN method can be used by remote sensing professionals to adjust or correct raw imaging data for changing environments to improve plant characterization.

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