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Dante R.A.,Embrapa Agricultural Informatics | Larkins B.A.,University of Nebraska - Lincoln | Larkins B.A.,University of Arizona | Sabelli P.A.,University of Arizona
Frontiers in Plant Science | Year: 2014

Seed development is a complex process that requires coordinated integration of many genetic, metabolic, and physiological pathways and environmental cues. Different cell cycle types, such as asymmetric cell division, acytokinetic mitosis, mitotic cell division, and endoreduplication, frequently occur in sequential yet overlapping manner during the development of the embryo and the endosperm, seed structures that are both products of double fertilization. Asymmetric cell divisions in the embryo generate polarized daughter cells with different cell fates. While nuclear and cell division cycles play a key role in determining final seed cell numbers, endoreduplication is often associated with processes such as cell enlargement and accumulation of storage metabolites that underlie cell differentiation and growth of the different seed compartments. This review focuses on recent advances in our understanding of different cell cycle mechanisms operating during seed development and their impact on the growth, development, and function of seed tissues. Particularly, the roles of core cell cycle regulators, such as cyclindependent-kinases and their inhibitors, the Retinoblastoma-Related/E2F pathway and the proteasome-ubiquitin system, are discussed in the contexts of different cell cycle types that characterize seed development. The contributions of nuclear and cellular proliferative cycles and endoreduplication to cereal endosperm development are also discussed. © 2014 Dante, Larkins and Sabelli. Source

Marin F.R.,Embrapa Agricultural Informatics | Marin F.R.,University of Sao Paulo | Jones J.W.,University of Florida
Scientia Agricola | Year: 2014

Dynamic simulation models can increase research efficiency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, difficulties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from five growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling efficiency, correlation coefficient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass. Source

Barbedo J.G.A.,Embrapa Agricultural Informatics
Plant Disease | Year: 2014

A method is presented to detect and quantify leaf symptoms using conventional color digital images. The method was designed to be completely automatic, eliminating the possibility of human error and reducing time taken to measure disease severity. The program is capable of dealing with images containing multiple leaves, further reducing the time taken. Accurate results are possible when the symptoms and leaf veins have similar color and shade characteristics. The algorithm is subject to one constraint: the background must be as close to white or black as possible. Tests showed that the method provided accurate estimates over a wide variety of conditions, being robust to variation in size, shape, and color of leaves; symptoms; and leaf veins. Low rates of false positives and false negatives occurred due to extrinsic factors such as issues with image capture and the use of extreme file compression ratios. © 2014 The American Phytopathological Society. Source

Barbedo J.G.A.,Embrapa Agricultural Informatics
Biosystems Engineering | Year: 2016

The problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades, however the techniques proposed so far are usually limited in their scope and dependent on ideal capture conditions in order to work properly. This apparent lack of significant advancements may be partially explained by some difficult challenges posed by the subject: presence of complex backgrounds that cannot be easily separated from the region of interest (usually leaf and stem), boundaries of the symptoms often are not well defined, uncontrolled capture conditions may present characteristics that make the image analysis more difficult, certain diseases produce symptoms with a wide range of characteristics, the symptoms produced by different diseases may be very similar, and they may be present simultaneously. This paper provides an analysis of each one of those challenges, emphasizing both the problems that they may cause and how they may have potentially affected the techniques proposed in the past. Some possible solutions capable of overcoming at least some of those challenges are proposed. © 2016 IAgrE. Source

Bonacin R.,CTI Renato Archer | Nabuco O.F.,CTI Renato Archer | Pierozzi Junior I.,Embrapa Agricultural Informatics
Future Generation Computer Systems | Year: 2015

Agriculture is both highly dependent on water resources, and impacting on these resources. Regardless of advances in the area, the impacts of water scarcity and climatic changes on agriculture, as well as the impacts of agriculture on water resources, remain uncertain. Potentially, collaborative systems can support the management and information sharing of multifaceted and large scale data sources, providing valuable and indispensable information for research. However, these solutions rely on semantic interoperability, the construction of complex knowledge representation models, as well as information recovery. This work describes interoperability issues in the engineering process of the OntoAgroHidro, an ontology that represents knowledge about impacts of agricultural activities and climatic changes on water resources. The paper presents representative scenarios and questions, and discusses the reuse and integration of concepts using knowledge visualization techniques. Experiments on the information recovery scenario point out the potential and limitations of the OntoAgroHidro. © 2015 Elsevier B.V. Source

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