Wheat hardness by near infrared (NIR) spectroscopy: New insights

The determination of wheat hardness by the evaluation of whole wheat grain would be of considerable value to the UK Milling Industry. Until now, accurate whole wheat grain hardness predictions by NIR spectroscopy have only been reported for North American wheats. By the evaluation of selected samples of UK and North American wheats this study showed that the prediction of whole wheat grain hardness by NTR spectroscopy depends only on the scattering properties of the sample and that there is no direct relationship with chemical composition. The scattering effect, in case of whole wheat grain reflectance and transmittance spectra, was found not to be multiplicative as in the case of ground wheat grain spectra. Empirical NIR spectroscopy calibrations are often performed without knowing what is measured or understanding the basis of the measurement. In other words the NIR spectrophotometer is often used as a "black box". Empirical calibrations were performed using three different software packages i.e. lnfrasoft International (ISI) Software, NIRSystems Spectral Analysis Software (NSAS) and UNSCRAMBLER. Successful NIR spectroscopy hardness measurements on ground wheat are based on light scattering. Separating the scattering effect from whole wheat grain spectra mathematically allowed predictions not significantly different to empirical calibrations, with the benefit of a theoretical explanation and fewer terms used. Although hardness predictions for whole wheat grain were not as accurate as in the case of ground wheat grain, it did prove to predict hardness with an acceptable accuracy with practical use as screening methods for grain trading. This study did not completely solve the problem of predicting whole wheat grain hardness by NIR spectroscopy, but new insights were provided which would hopefully encourage further work in this area and lead to a more complete fundamental understanding of the properties of whole wheat grain hardness using NIR spectroscopy

Detecting wide lines using isotropic nonlinear filtering

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos

[eng] World cereal stocks need to increase in order to meet growing demands. Currently, maize, rice, wheat, are the main crops worldwide, while other cereals such as barley, sorghum, oat or different millets are also well placed in the top list. Crop productivity is affected directly by climate change factors such as heat, drought, floods or storms. Researchers agree that global climate change is having a major impact on crop productivity. In that way, several studies have been focused on climate change scenarios and more specifically abiotic stresses in cereals. For instance, in the case of heat stress, high temperatures between anthesis to grain filling can decrease grain yield. In order to deal with the climate change and future environmental scenarios, plant breeding is one of the main alternatives breeding is even considered to contribute to the larger component of yield growth compared to management. Plant breeding programs are focused on identifying genotypes with high yields and quality to act as a parentals and further the best individuals among the segregating population thus develop new varieties of plants. Breeders use the phenotypic data, plant and crop performance, and genetic information to improve the yield by selection (GxE, with G and E indicating genetic and environmental factors). More factors must be taken into account to increase the yield, such as, for instance, the education of farmers, economic incentives and the use of new technologies (GxExM, with M indicating management). Plant phenotyping is related with the observable (or measurable) characteristics of the plant while the crop growing as well as the association between the plant genetic background and its response to the environment (GxE). In traditional phenotyping the measurements are collated manually, which is tedious, time consuming and prone to subjective errors. Nowadays the technology is involved in many applications. From the point of view of plan phenotyping, technology has been incorporated as a tool. The use of image processing techniques integrating sensors and algorithm processes, is therefore, an alternative to asses automatically (or semi-automatically) these traits. Images have become a useful tool for plant phenotyping because most frequently data from the sensors are processed and analyzed as an image in two (2D) or three (3D) dimensions. An image is the arrangement of pixels in a regular Cartesian coordinates as a matrix, each pixel has a numerical value into the matrix which represents the number of photons captured by the sensor within the exposition time. Therefore, an image is the optical representation of the object illuminated by a radiating source. The main characteristics of images can be defined by the sensor spectral and spatial properties, with the spatial properties of the resulting image also heavily dependent on the sensor platform (which determines the distance from the target object).[spa] Las existencias mundiales de cereales deben aumentar para satisfacer la creciente demanda. Actualmente, el maíz, el arroz y el trigo son los principales cultivos a nivel mundial, otros cereales como la cebada, el sorgo y la avena están también bien ubicados en la lista. La productividad de los cultivos se ve afectada directamente por factores del cambio climático como el calor, la sequía, las inundaciones o las tormentas. Los investigadores coinciden en que el cambio climático global está teniendo un gran impacto en la productividad de los cultivos. Es por esto que muchos estudios se han centrado en escenarios de cambio climático y más específicamente en estrés abiótico. Por ejemplo, en el caso de estrés por calor, las altas temperaturas entre antesis y llenado de grano pueden disminuir el rendimiento del grano. Para hacer frente al cambio climático y escenarios ambientales futuros, el mejoramiento de plantas es una de las principales alternativas; incluso se considera que las técnicas de mejoramiento contribuyen en mayor medida al aumento del rendimiento que el manejo del cultivo. Los programas de mejora se centran en identificar genotipos con altos rendimientos y calidad para actuar como progenitores y promover los mejores individuos para desarrollar nuevas variedades de plantas. Los mejoradores utilizan los datos fenotípicos, el desempeño de las plantas y los cultivos, y la información genética para mejorar el rendimiento mediante selección (GxE, donde G y E indican factores genéticos y ambientales). El fenotipado plantas está relacionado con las características observables (o medibles) de la planta mientras crece el cultivo, así como con la asociación entre el fondo genético de la planta y su respuesta al medio ambiente (GxE). En el fenotipado tradicional, las mediciones se clasifican manualmente, lo cual es tedioso, consume mucho tiempo y es propenso a errores subjetivos. Sin embargo, hoy en día la tecnología está involucrada en muchas aplicaciones. Desde el punto de vista del fenotipado de plantas, la tecnología se ha incorporado como una herramienta. El uso de técnicas de procesamiento de imágenes que integran sensores y algoritmos son por lo tanto una alternativa para evaluar automáticamente (o semiautomáticamente) estas características

Integrating Public and Private Data Sources for Freight Transportation Planning

The Moving Ahead for Progress in the 21st Century Act (MAP-21) stipulates that state transportation agencies expand their interest in freight initiatives and modeling to support planning efforts, particularly the evaluation of current and future freight transportation capacity necessary to ensure freight mobility. However, the understanding of freight demand and the evaluation of current and future freight transportation capacity are not only determined by robust models, but are critically contingent on the availability of accurate data. Effective partnerships are clearly needed between the public and private sectors to ensure adequate freight planning and funding of transportation infrastructure at the state and local levels. However, establishing partnerships with firms who are both busy and suspicious of data-sharing, remains a challenge. This study was commissioned by the Texas Department of Transportation (TxDOT) to explore the feasibility of TxDOT entering into a data-sharing partnership with representatives of the private sector to obtain sample data for use in formulating a strategy for integrating public and private sector data sources. This report summarizes the findings, lessons learned, and recommendations formed from the outreach effort, and provides a prototype freight data architecture that will facilitate the storage, exchange, and integration of freight data through a data-sharing partnership.Texas Department of Transportation Research and Technology Implementation Office P.O. Box 5080 Austin, TX 78763-5080Civil, Architectural, and Environmental Engineerin

Assessment of Grain Safety in Developing Nations

Grains are the most widely consumed foods worldwide, with maize (Zea mays) being frequently consumed in developing countries where it feeds approximately 900 million people under the poverty line of 2 USD per day. While grain handling practices are acceptable in most developed nations, many developing nations still face challenges such as inadequate field management, drying, and storage. Faulty grain handling along with unavoidably humid climates result in recurrent fungal growth and spoilage, which compromises both the end-quality and safety of the harvest. This becomes particularly problematic where there is little awareness about health risks associated with poor quality grain. Fungi are contaminants of maize and some can produce toxins, known as mycotoxins, that both devalue crop marketability and have detrimental health effects, especially to those malnourished. As some households depend on their harvest for self-consumption, losses due to fungi endanger their food security. To abate the threat posed by mycotoxigenic fungi on maize among developing nations, this research was conducted as a compilation of works in several countries. More specifically, it describes agricultural practices currently in use in developing nations, provides an overview of mycotoxin prevalence and approaches that can be used to improve grain safety post-harvest through proper storage. Additionally, it provides a platform to evaluate the economic feasibility of storage technologies for maize storage at household level. While the countries of focus were Guatemala, Honduras and Nepal, findings presented can lead to improved decision-making within any maize production chain to safeguard consumers throughout the developing world. Advisor: Andréia Bianchin

Creating a model Hazard Analysis Critical Control Point (HACCP) system within the flour milling industry

Foodborne illnesses and diseases have been a major concern to the food industry worldwide. Foodborne illnesses are caused by several disease-causing germs that have contaminated food. Researchers have identified over 250 foodborne diseases. Most of them are infectious, caused by various bacteria; and parasites. Chemicals and harmful toxins are other factors that can contaminate food and cause foodborne illnesses. The top viruses that cause foodborne illnesses in the US are norovirus, salmonella, clostridium perfringens, campylobacter, and staphylococcus aureus (staph). Some germs do not cause as many illnesses. However, when they do, they have a higher risk of causing individuals to end up hospitalized. Those viruses include Clostridium botulinum (botulism), listeria, Escherichia coli (E. coli), and vibrio (Hedberg, 1999). According to the Centers for Disease Control and Prevention (CDC), they estimate that 48 million people get sick from a foodborne illness, 128,000 get hospitalized, and 3,000 die each year (FDA 2022). The purpose of this study is to show how the Hazard Analysis Critical Control Point (HACCP) system has been proven to be effective in the food industry. It is a food safety tool that manages the hazards associated with food production plants and farm-to-table within the past three decades, the U.S. government agencies have issued a succession of regulations that require a HACCP plan development for certain types of foods. The HACCP plan system is a proactive control approach and is globally accepted for manufacturers to prevent recalls and outbreaks, along with reducing financial losses (Gillion 2005)

An Integrated Crop- and Soil-Based Strategy for Variable-Rate Nitrogen Management in Corn

Nitrogen (N) management in cereal crops has been the subject of considerable research and debate for several decades. Historic N management practices have contributed to low nitrogen use efficiency (NUE). Low NUE can be caused by such things as poor synchronization between soil N supply and crop demand, uniform application rates of fertilizer N to spatially variable landscapes, and failure to account for temporally variable influences on soil N supply and crop N need. Active canopy reflectance sensors and management zones (MZ) have been studied separately as possible plant- and soil-based N management tools to increase NUE. Recently, some have suggested that the integration of these two approaches would provide a more robust N management strategy that could more effectively account for soil and plant effects on crop N need. For this reason, the goal of this research was to develop an N application strategy that would account for spatial variability in soil properties and use active canopy reflectance sensors to determine in-season, on-the-go N fertilizer rates, thereby increasing NUE and economic return for producers over current N management practices. To address this overall goal, a series of studies were conducted to better understand active canopy sensor use and explore the possibility of integrating spatial soil data with active canopy sensors. Sensor placement to assess crop N status was first examined. It was found that the greatest reduction in error over sensing each individual row for a hypothetical 24-row applicator was obtained with 2-3 sensors estimating an average chlorophyll index for the entire boom width. Next, use of active sensor-based soil organic matter (OM) estimation was compared to more conventional aerial image-based soil OM estimation. By adjusting regression intercept values for each field, OM could be predicted using either a single sensor or image data layer. The final study consisted of validation of the active sensor algorithm developed by Solari (2006), identification of soil variables for MZ delineation, and the possible integration of MZ and active sensors for N application. Crop response (sensor measured sufficiency index and yield) had the highest correlation with soil optical reflectance readings in sandy fields and with apparent soil electrical conductivity in silt loam fields with eroded slopes. Therefore, using these soil variables to delineate MZ allowed characterization of spatial patterns in both in-season crop response (sufficiency index) and yield. Compared to uniform N application, integrating MZ and sensor-based N application resulted in substantial N savings (~40-120 kg ha-1) and increases in partial factor productivity (~13-75 kg grain (kg N applied)-1) for fine-textured soils with eroded slopes. However, for coarser texture soils the current sensor-based N application algorithm may require further calibration, and for fields with no spatial variability there appears to be no benefit to using the algorithm. Collectively, results from these studies show promise for integrating active sensor-based N application and static soil-based MZ to increase NUE and economic return for producers over current N management strategies, but further research is needed to explore how best to integrate these two N management strategies

Maize Tassel Detection From UAV Imagery Using Deep Learning

The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on the imagery collected from an unmanned aerial vehicle, using deep learning models. The first approach was a customized framework for tassel detection based on convolutional neural network (TD-CNN). The other method was a state-of-the-art object detection technique of the faster region-based CNN (Faster R-CNN), serving as baseline detection accuracy. The evaluation criteria for tassel detection were customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9% and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection