Computer vision classification of barley flour based on spatial pyramid partition ensemble

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples' classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification1913CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ420562/2018-

Classification of fermented cocoa beans (cut test) using computer vision

Fermentation of cocoa beans is a critical step for chocolate manufacturing, since fermentation influences the development of flavour, affecting components such as free amino acids, peptides and sugars. The degree of fermentation is determined by visual inspection of changes in the internal colour and texture of beans, through the cut-test. Although considered standard for evaluation of fermentation in cocoa beans, this method is time consuming and relies on specialized personnel. Therefore, this study aims to classify fermented cocoa beans using computer vision as a fast and accurate method. Imaging and image analysis provides hand-crafted features computed from the beans, that were used as predictors in random decision forests to classify the samples. A total of 1800 beans were classified into four grades of fermentation. Concerning all image features, 0.93 of accuracy was obtained for validation of unbalanced dataset, with precision of 0.85, recall of 0.81. Although the unbalanced dataset represents actual variation of fermentation, the method was tested for a balanced dataset, to investigate the influence of a smaller number of samples per class, obtaining 0.92, 0.92 and 0.90 for accuracy, precision and recall, respectively. The technique can evolve into an industrial application with a proper integration framework, substituting the traditional method to classify fermented cocoa beans

Identification of Copper in Stems and Roots of Jatropha curcas L. by Hyperspectral Imaging

The in situ determination of metals in plants used for phytoremediation is still a challenge that must be overcome to control the plant stress over time due to metals uptake as well as to quantify the concentration of these metals in the biomass for further potential applications. In this exploratory study, we acquired hyperspectral images in the visible/near infrared regions of dried and ground stems and roots of Jatropha curcas L. to which different amounts of copper (Cu) were added. The spectral information was extracted from the images to build classification models based on the concentration of Cu. Optimum wavelengths were selected from the peaks and valleys showed in the loadings plots resulting from principal component analysis, thus reducing the number of spectral variables. Linear discriminant analysis was subsequently performed using these optimum wavelengths. It was possible to differentiate samples without addition of copper from samples with low (0.5–1% wt.) and high (5% wt.) amounts of copper (83.93% accuracy, >0.70 sensitivity and specificity). This technique could be used after enhancing prediction models with a higher amount of samples and after determining the potential interference of other compounds present in plants.University of Seville VIPPIT-2019-I.5University of Seville VIPPIT-2019-I.

Modern Seed Technology

Satisfying the increasing number of consumer demands for high-quality seeds with enhanced performance is one of the most imperative challenges of modern agriculture. In this view, it is essential to remember that the seed quality of crops does not improve

Inférence des réseaux de régulation de la synthèse des protéines de réserve du grain de blé tendre (Triticum aestivum L.) en réponse à l'approvisionnement en azote et en soufre: Inférence des réseaux de régulation de la synthèse des protéines de réserve du grain de blé tendre (Triticum aestivum L.) en réponse à l'approvisionnement en azote et en soufre

Grain storage protein content and composition are the main determinants of bread wheat (Triticum aestivum L.) end-use value. Scaling laws governing grain protein composition according to grain nitrogen and sulfur content could be the outcome of a finely tuned regulation network. Although it was demonstrated that the main regulation of grain storage proteins accumulation occurs at the transcriptomic level in cereals, knowledge of the underlying molecular mechanisms is elusive. Moreover, the effects of nitrogen and sulfur on these mechanisms are unknown. The issue of skyrocketing data generation in research projects is addressed by developing high-throughput bioinformatics approaches. Extracting knowledge on from such massive amounts of data is therefore an important challenge. The work presented herein aims at elucidating regulatory networks involved in grain storage protein synthesis and their response to nitrogen and sulfur supply using a rule discovery approach. This approach was extended, implemented in the form of a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data. This platform allowed us to define different semantics in a comprehensive framework; each semantic having its own biological meaning, thus providing us with global informative networks. Spatiotemporal specificity of transcription factors expression was observed and particular attention was paid to their relationship with grain storage proteins in the inferred networks. The work initiated here opens up a field of innovative investigation to identify new targets for plant breeding and for an improved end-use value and nutritional quality of wheat in the context of inputs limitation. Further analyses should enhance the understanding of the control of grain protein composition and allow providing wheat adapted to specific uses or deficient in protein fractions responsible for gluten allergenicity and intolerance.La teneur et la composition en protéines de réserve du grain de blé tendre (Triticum aestivum L.) sont les principaux déterminants de sa valeur d’usage et de sa qualité nutritionnelle. La composition en protéines de réserve du grain est déterminée par la teneur en assimilâts azotés et soufrés par grain via des lois d’échelle qui pourraient être les propriétés émergentes de réseaux de régulation. Plusieurs facteurs de transcription intervenant dans cette régulation ont été mis en évidence, mais les voies et mécanismes impliqués sont encore très peu connus. Le constat est identique en ce qui concerne l’impact de la nutrition azotée et soufrée sur ce réseau de régulation. Le développement des outils de génomique fonctionnelle et de bioinformatique permet aujourd’hui d’aborder ces régulations de manière globale via une approche systémique mettant en relation plusieurs niveaux de régulation. L’objectif du travail présenté est d’explorer les réseaux de régulation –omiques impliqués dans le contrôle de l’accumulation des protéines de réserve dans le grain de blé tendre et leur réponse à l’approvisionnement en azote et en soufre. Une approche d’inférence de réseaux basée sur la découverte de règles a été étendue, implémentée sous la forme d’une plateforme web. L’utilisation de cette plateforme a permis de définir des sémantiques multiples afin d’inférer dans un cadre global, des règles possédant différentes significations biologiques. Des facteurs de transcription spécifiques de certains organes et certaines phases de développement ont été mis en évidence et un intérêt particulier a été apporté à leur position dans les réseaux de règles inférés, notamment en relation avec les protéines de réserve. Les travaux initiés dans cette thèse ouvrent un champ d’investigation innovant pour l’identification de nouvelles cibles de sélection variétale pour l’amélioration de la valeur technologique et de la qualité nutritionnelle du blé. Ils devraient ainsi permettre de mieux maîtriser la composition en protéines de réserve et ainsi produire des blés adaptés à des utilisations ciblées ou carencé en certaines fractions protéiques impliquées dans des phénomènes d’allergénicité et d’intolérance du gluten, ce dans un contexte d’agriculture durable et plus économe en intrants

Wheat Improvement

This open-access textbook provides a comprehensive, up-to-date guide for students and practitioners wishing to access in a single volume the key disciplines and principles of wheat breeding. Wheat is a cornerstone of food security: it is the most widely grown of any crop and provides 20% of all human calories and protein. The authorship of this book includes world class researchers and breeders whose expertise spans cutting-edge academic science all the way to impacts in farmers’ fields. The book’s themes and authors were selected to provide a didactic work that considers the background to wheat improvement, current mainstream breeding approaches, and translational research and avant garde technologies that enable new breakthroughs in science to impact productivity. While the volume provides an overview for professionals interested in wheat, many of the ideas and methods presented are equally relevant to small grain cereals and crop improvement in general. The book is affordable, and because it is open access, can be readily shared and translated -- in whole or in part -- to university classes, members of breeding teams (from directors to technicians), conference participants, extension agents and farmers. Given the challenges currently faced by academia, industry and national wheat programs to produce higher crop yields --- often with less inputs and under increasingly harsher climates -- this volume is a timely addition to their toolkit

Scientific, Health and Social Aspects of the Food Industry

This book presents the wisdom, knowledge and expertise of the food industry that ensures the supply of food to maintain the health, comfort, and wellbeing of humankind. The global food industry has the largest market: the world population of seven billion people. The book pioneers life-saving innovations and assists in the fight against world hunger and food shortages that threaten human essentials such as water and energy supply. Floods, droughts, fires, storms, climate change, global warming and greenhouse gas emissions can be devastating, altering the environment and, ultimately, the production of foods. Experts from industry and academia, as well as food producers, designers of food processing equipment, and corrosion practitioners have written special chapters for this rich compendium based on their encyclopedic knowledge and practical experience. This is a multi-authored book. The writers, who come from diverse areas of food science and technology, enrich this volume by presenting different approaches and orientations