The Experts below are selected from a list of 17847 Experts worldwide ranked by ideXlab platform
J. A. Moses - One of the best experts on this subject based on the ideXlab platform.
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machine vision system for food Grain Quality evaluation a review
Trends in Food Science and Technology, 2016Co-Authors: P. Vithu, J. A. MosesAbstract:Abstract Background Quality of pre-processed food Grains is a critical aspect and a major decider of market acceptability, storage stability, processing Quality, and overall consumer acceptance. Among various indices of food Grain Quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the Grain. Conventional method of Grain Quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy. Scope and approach Machine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for Quality evaluation of numerous agricultural products. This note provides an up-to-date review on the major applications of machine vision systems for Grain Quality evaluation applications in non-touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems. Key findings and conclusions Machine vision systems can provide rapid and accurate information about external Quality aspects of food Grains. However, it is a task to integrate such systems with those that can explain internal Grain Quality attributes. In the near future, with ever-growing application requirements and research developments, machine vision systems can provide effective solutions for various Grain Quality evaluation applications.
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Machine vision system for food Grain Quality evaluation: A review
Trends in Food Science & Technology, 2016Co-Authors: P. Vithu, J. A. MosesAbstract:BACKGROUND\nQuality of pre-processed food Grains is a critical aspect and a major decider of market acceptability, storage stability, processing Quality, and overall consumer acceptance. Among various indices of food Grain Quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the Grain. Conventional method of Grain Quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy. \n\nSCOPE AND APPROACH\nMachine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for Quality evaluation of numerous agricultural products. This note provides an up-to-date review on the major applications of machine vision systems for Grain Quality evaluation applications in non-touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems. \n\nKEY FINDINGS AND CONCLUSIONS\nMachine vision systems can provide rapid and accurate information about external Quality aspects of food Grains. However, it is a task to integrate such systems with those that can explain internal Grain Quality attributes. In the near future, with ever-growing application requirements and research developments, machine vision systems can provide effective solutions for various Grain Quality evaluation applications.
P. Vithu - One of the best experts on this subject based on the ideXlab platform.
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machine vision system for food Grain Quality evaluation a review
Trends in Food Science and Technology, 2016Co-Authors: P. Vithu, J. A. MosesAbstract:Abstract Background Quality of pre-processed food Grains is a critical aspect and a major decider of market acceptability, storage stability, processing Quality, and overall consumer acceptance. Among various indices of food Grain Quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the Grain. Conventional method of Grain Quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy. Scope and approach Machine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for Quality evaluation of numerous agricultural products. This note provides an up-to-date review on the major applications of machine vision systems for Grain Quality evaluation applications in non-touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems. Key findings and conclusions Machine vision systems can provide rapid and accurate information about external Quality aspects of food Grains. However, it is a task to integrate such systems with those that can explain internal Grain Quality attributes. In the near future, with ever-growing application requirements and research developments, machine vision systems can provide effective solutions for various Grain Quality evaluation applications.
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Machine vision system for food Grain Quality evaluation: A review
Trends in Food Science & Technology, 2016Co-Authors: P. Vithu, J. A. MosesAbstract:BACKGROUND\nQuality of pre-processed food Grains is a critical aspect and a major decider of market acceptability, storage stability, processing Quality, and overall consumer acceptance. Among various indices of food Grain Quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the Grain. Conventional method of Grain Quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy. \n\nSCOPE AND APPROACH\nMachine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for Quality evaluation of numerous agricultural products. This note provides an up-to-date review on the major applications of machine vision systems for Grain Quality evaluation applications in non-touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems. \n\nKEY FINDINGS AND CONCLUSIONS\nMachine vision systems can provide rapid and accurate information about external Quality aspects of food Grains. However, it is a task to integrate such systems with those that can explain internal Grain Quality attributes. In the near future, with ever-growing application requirements and research developments, machine vision systems can provide effective solutions for various Grain Quality evaluation applications.
Glenn J Fitzgerald - One of the best experts on this subject based on the ideXlab platform.
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models of Grain Quality in wheat a review
Field Crops Research, 2017Co-Authors: J G Nuttall, Garry Oleary, J F Panozzo, C K Walker, K M Barlow, Glenn J FitzgeraldAbstract:Maintaining Grain Quality of wheat under climate change is critical for human nutrition, end-use functional properties, as well as commodity value. This paper reviews the current knowledge of high temperature and elevated atmospheric CO2 on whole-Grain and functional properties of wheat. It also considers the utility of contemporary crop models for investigating the impacts of climate change on wheat Quality; and discusses opportunities for advancing model capability. Under elevated CO2 wheat yield can increase by up to 36%, but universally Grain protein concentration decreases and a shift in composition translates to reduced functional properties. High temperature during the post-anthesis period of crops can cause a step change reduction in Grain-set, Grain size and milling yield. Numerous crop models including APSIM-Nwheat, CropSyst, Sirius, GLAM-HTS account for high CO2 effects through modification of RUE, TE or critical leaf-N concentration and high temperature by accelerated leaf senescence, Grain number, potential Grain weight or HI modifications. For Grain Quality, however, crop models are typically restricted to predicting average Grain size and Grain-N content (concentration), although the SiriusQuality model accounts for the major storage proteins, gliadin and glutenin. For protein composition, high temperature stress reduces the glutenin/gliadin ratio and limits the synthesis of the larger SDS-insoluble glutenin polymers which causes wheat dough to have weaker viscoelasticity properties. This link provides an opportunity to model high temperature effects on Grain functional properties. Further development and testing, utilizing Grain Quality data from global FACE programmes will be particularly valuable for validating and enhancing the performance of such models. For whole-Grain characteristics, a single-spike model approach, which accounts for intra-spike variation in assimilate deposition may provide an opportunity to predict Grain size distribution and associated screenings percentage and milling yield. Taken together expanding the predictive capability of our crop models to Grain Quality is an important step in providing a powerful tool for developing adaptation strategies for combating the impacts of climate change to global crop production and Grain Quality.
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Models of Grain Quality in wheat—A review
Field Crops Research, 2017Co-Authors: J G Nuttall, J F Panozzo, C K Walker, K M Barlow, Garry O'leary, Glenn J FitzgeraldAbstract:Maintaining Grain Quality of wheat under climate change is critical for human nutrition, end-use functional properties, as well as commodity value. This paper reviews the current knowledge of high temperature and elevated atmospheric CO2 on whole-Grain and functional properties of wheat. It also considers the utility of contemporary crop models for investigating the impacts of climate change on wheat Quality; and discusses opportunities for advancing model capability. Under elevated CO2 wheat yield can increase by up to 36%, but universally Grain protein concentration decreases and a shift in composition translates to reduced functional properties. High temperature during the post-anthesis period of crops can cause a step change reduction in Grain-set, Grain size and milling yield. Numerous crop models including APSIM-Nwheat, CropSyst, Sirius, GLAM-HTS account for high CO2 effects through modification of RUE, TE or critical leaf-N concentration and high temperature by accelerated leaf senescence, Grain number, potential Grain weight or HI modifications. For Grain Quality, however, crop models are typically restricted to predicting average Grain size and Grain-N content (concentration), although the SiriusQuality model accounts for the major storage proteins, gliadin and glutenin. For protein composition, high temperature stress reduces the glutenin/gliadin ratio and limits the synthesis of the larger SDS-insoluble glutenin polymers which causes wheat dough to have weaker viscoelasticity properties. This link provides an opportunity to model high temperature effects on Grain functional properties. Further development and testing, utilizing Grain Quality data from global FACE programmes will be particularly valuable for validating and enhancing the performance of such models. For whole-Grain characteristics, a single-spike model approach, which accounts for intra-spike variation in assimilate deposition may provide an opportunity to predict Grain size distribution and associated screenings percentage and milling yield. Taken together expanding the predictive capability of our crop models to Grain Quality is an important step in providing a powerful tool for developing adaptation strategies for combating the impacts of climate change to global crop production and Grain Quality.
Rosa Paula Cuevas - One of the best experts on this subject based on the ideXlab platform.
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rice Grain Quality and consumer preferences a case study of two rural towns in the philippines
PLOS ONE, 2016Co-Authors: Rosa Paula Cuevas, Valerien O Pede, Justin D Mckinley, Orlee Velarde, Matty DemontAbstract:Hedonic pricing analysis is conducted to determine the implicit values of various attributes in the market value of a good. In this study, hedonic pricing analysis was applied to measure the contribution of Grain Quality search and experience attributes to the price of rice in two rural towns in the Philippines. Rice samples from respondents underwent quantitative routine assessments of Grain Quality. In particular, gelatinization temperature and chalkiness, two parameters that are normally assessed through visual scores, were evaluated by purely quantitative means (differential scanning calorimetry and by digital image analysis). Results indicate that rice consumed by respondents had mainly similar physical and chemical Grain Quality attributes. The respondents’ revealed preferences were typical of what has been previously reported for Filipino rice consumers. Hedonic regression analyses showed that Grain Quality characteristics that affected price varied by income class. Some of the traits or socioeconomic factors that affected price were percent broken Grains, gel consistency, and household per capita rice consumption. There is an income effect on rice price and the characteristics that affect price vary between income classes.
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designing climate resilient rice with ideal Grain Quality suited for high temperature stress
Journal of Experimental Botany, 2015Co-Authors: Nese Sreenivasulu, Vito M. Butardo, Gopal Misra, Rosa Paula Cuevas, Roslen Anacleto, Polavarpu Kavi B KishorAbstract:To ensure rice food security, the target outputs of future rice breeding programmes should focus on developing climate-resilient rice varieties with emphasis on increased head rice yield coupled with superior Grain Quality. This challenge is made greater by a world that is increasingly becoming warmer. Such environmental changes dramatically impact head rice and milling yield as well as increasing chalkiness because of impairment in starch accumulation and other storage biosynthetic pathways in the Grain. This review highlights the knowledge gained through gene discovery via quantitative trait locus (QTL) cloning and structural–functional genomic strategies to reduce chalk, increase head rice yield, and develop stable lines with optimum Grain Quality in challenging environments. The newly discovered genes and the knowledge gained on the influence of specific alleles related to stability of Grain Quality attributes provide a robust platform for marker-assisted selection in breeding to design heat-tolerant rice varieties with superior Grain Quality. Using the chalkiness trait in rice as a case study, we demonstrate here that the emerging field of systems genetics can help fast-track the identification of novel alleles and gene targets that can be pyramided for the development of environmentally robust rice varieties that possess improved Grain Quality.
Nese Sreenivasulu - One of the best experts on this subject based on the ideXlab platform.
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Improving Rice Grain Quality: State-of-the-Art and Future Prospects.
Methods of Molecular Biology, 2018Co-Authors: Vito M. Butardo, Nese Sreenivasulu, Bienvenido O. JulianoAbstract:: Rice Grain Quality encompasses complex interrelated traits that cover biochemical composition, cooking, eating, nutritional, and sensory properties. Because rice endosperm is composed mainly of starch, rice Grain Quality is traditionally defined by characterizing starch structure and composition, which is then subsequently correlated with functional properties of the Grain. The current proxy tests routinely used to describe rice Grain Quality preferences are rather limited to the estimation of apparent amylose content, gelatinization temperature, and gel consistency. Additional tests that characterize starch property, viscoelasticity, Grain texture, and aroma are also employed in more advanced laboratories. However, these tests are not routinely applied in breeding programs to distinguish cooking Quality classes to reflect evolving consumer preference and market demand. As consumer preferences in Asia and all over the world are diverse due to varied demographics and culture, defining uniform attributes to capture regional Grain Quality preferences becomes more challenging. Hence, novel and innovative proxy tests are needed to characterize rice Grain Quality to meet the demand for consumer preferences of commercially-released cultivars. In this chapter, the current methods employed in rice Grain Quality monitoring are succinctly reviewed. Future prospects for improvement are identified, introducing cutting edge technologies that can facilitate high-throughput screening of rice diversity panels and breeding lines. Aside from addressing the requirements for Quality improvement in the traditional inbred rice breeding programs, we also tackled the need to enhance Grain Quality in the hybrid rice sector.
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designing climate resilient rice with ideal Grain Quality suited for high temperature stress
Journal of Experimental Botany, 2015Co-Authors: Nese Sreenivasulu, Vito M. Butardo, Gopal Misra, Rosa Paula Cuevas, Roslen Anacleto, Polavarpu Kavi B KishorAbstract:To ensure rice food security, the target outputs of future rice breeding programmes should focus on developing climate-resilient rice varieties with emphasis on increased head rice yield coupled with superior Grain Quality. This challenge is made greater by a world that is increasingly becoming warmer. Such environmental changes dramatically impact head rice and milling yield as well as increasing chalkiness because of impairment in starch accumulation and other storage biosynthetic pathways in the Grain. This review highlights the knowledge gained through gene discovery via quantitative trait locus (QTL) cloning and structural–functional genomic strategies to reduce chalk, increase head rice yield, and develop stable lines with optimum Grain Quality in challenging environments. The newly discovered genes and the knowledge gained on the influence of specific alleles related to stability of Grain Quality attributes provide a robust platform for marker-assisted selection in breeding to design heat-tolerant rice varieties with superior Grain Quality. Using the chalkiness trait in rice as a case study, we demonstrate here that the emerging field of systems genetics can help fast-track the identification of novel alleles and gene targets that can be pyramided for the development of environmentally robust rice varieties that possess improved Grain Quality.
