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Barbedo J.G.A.,Embrapa Agricultural Informatics
European Journal of Plant Pathology | Year: 2016

The segmentation of symptoms during image analysis of diseased plant leaves is an essential process for detection and classification of diseases. However, there are challenges involved in the task, many of them related to the variability of image and host/symptom characteristics and conditions. As a result of those challenges, the methods proposed in the literature so far focus on a specific problem and are usually bounded by tight constraints regarding image capture conditions. This research explores a new automatic method for segmenting disease symptoms on plant leaves that was designed to be applicable in a wide range of situations. The proposed technique employs only color channel manipulations and Boolean operations applied on binary masks, thus being simpler and more robust compared to many previously described automatic methods. Its effectiveness is demonstrated by tests performed over a large database containing images of 77 different diseases of 11 plant species. A comparison with manual segmentation is also presented, further reinforcing the advantages of the proposed approach. © 2016 Koninklijke Nederlandse Planteziektenkundige Vereniging

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.

Santos T.T.,Embrapa Agricultural Informatics | Rodrigues G.C.,Embrapa Agricultural Informatics
Machine Vision and Applications | Year: 2015

The three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and three-dimensional models are publicly available. © 2015 Springer-Verlag Berlin Heidelberg

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.

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.

Barbedo J.G.A.,Embrapa Agricultural Informatics
Tropical Plant Pathology | Year: 2016

A new computer algorithm is proposed to differentiate signs and symptoms of plant disease from asymptomatic tissues in plant leaves. The simple algorithm manipulates the histograms of the H (from HSV color space) and a (from the L*a*b* color space) color channels. All steps in the algorithmic process are automatic, with the exception of the final step in which the user decides which channel (H or a) provides the better differentiation. An in-depth analysis of the problem of disease symptom differentiation is also presented, in which issues such as lesion delimitation, illumination, leaf venation interference, leaf ruggedness, among others, are thoroughly discussed. The proposed algorithm was tested under a wide variety of conditions, which included 19 plant species, 82 diseases, and images gathered under controlled and uncontrolled environmental conditions. The algorithm proved useful for a wide variety of plant diseases and conditions, although some situations may require alternative solutions. © 2016, Sociedade Brasileira de Fitopatologia.

Marin F.R.,Embrapa Agricultural Informatics | Jones J.W.,University of Florida | Singels A.,South African Sugarcane Research Institute | Royce F.,University of Florida | And 3 more authors.
Climatic Change | Year: 2013

This study evaluated the effects of climate change on sugarcane yield, water use efficiency, and irrigation needs in southern Brazil, based on downscaled outputs of two general circulation models (PRECIS and CSIRO) and a sugarcane growth model. For three harvest cycles every year, the DSSAT/CANEGRO model was used to simulate the baseline and four future climate scenarios for stalk yield for the 2050s. The model was calibrated for the main cultivar currently grown in Brazil based on five field experiments under several soil and climate conditions. The sensitivity of simulated stalk fresh mass (SFM) to air temperature, CO2 concentration [CO2] and rainfall was also analyzed. Simulated SFM responses to [CO2], air temperature and rainfall variations were consistent with the literature. There were increases in simulated SFM and water usage efficiency (WUE) for all scenarios. On average, for the current sugarcane area in the State of São Paulo, SFM would increase 24 % and WUE 34 % for rainfed sugarcane. The WUE rise is relevant because of the current concern about water supply in southern Brazil. Considering the current technological improvement rate, projected yields for 2050 ranged from 96 to 129 t ha-1, which are respectively 15 and 59 % higher than the current state average yield. © 2012 The Author(s).

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.

Barbedo J.G.A.,Embrapa Agricultural Informatics
Journal of Asia-Pacific Entomology | Year: 2014

This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparison with the manual approach. The proposed algorithm is capable of detecting and quantifying not only adult whiteflies, but also specimens in the nymph stage. A complete performance evaluation is presented, with emphasis on the conditions and situations for which the algorithm succeeds, and also on the circumstances that need further work. Although this proposal was entirely developed using soybean leaves, it can be easily extended to other kinds of crops with little or no changes in the algorithm. The system employs only widely used image processing operations, so it can be easily implemented in any image processing software package. © 2014 Korean Society of Applied Entomology, Taiwan Entomological Society and Malaysian Plant Protection Society.

Barbedo J.G.A.,Embrapa Agricultural Informatics | Tibola C.S.,Embrapa Wheat | Fernandes J.M.C.,Embrapa Wheat
Biosystems Engineering | Year: 2015

Because of the health risks associated with the ingestion of the mycotoxin deoxynivalenol (DON) produced by Fusarium head blight (FHB), improving its detection in wheat kernels is a major research goal. Currently, assessments are largely performed visually by human experts. Being subjective, such assessments may not always be consistent or entirely reliable. As a result, methods with a higher degree of objectivity have been investigated, and special attention has been dedicated to the use of hyperspectral imaging (HSI) as the basis for more reliable detection strategies. This paper presents an algorithm for automatic detection of FHB in wheat kernels using HSI. The goal was to develop a simple and accurate algorithm which gave as output an index that can be interpreted as the likelihood of the kernel being infected by FHB. With a classification accuracy above 91%, the developed algorithm was robust to factors such as shape, orientation, shadowing and clustering of kernels. It was shown that the algorithm was not only suitable for detecting FHB, but it also has the capability, albeit limited, of estimating DON concentrations in wheat kernels. © 2015 IAgrE.

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