Zea mays L. hybrids kernels evaluated by image analysis tools

A high-throughput X-ray micro-computed tomography (μCT) approach for measuring single kernel maize (Zea mays L.) volumes and densities

Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

This chapter explores the need for automation processes in information extraction from remote sensing images. This exploration is further extended by developing an ontological framework for geographic object-based image analysis (GEOBIA). GEOBIA provides a method to identify real-world geographic objects from remote sensing imagery. It uses the same technique as a human does to perceive different geo-objects and to distinguish them. Thus, human involvement in the form of expert knowledge will be required during image object identification. This need for human intervention is addressed by employing a knowledge representation language such as ontology to formalize human expert knowledge. After formalization, necessary rule sets for image interpretation are developed. Using rule-based classification, no human assistance is required at the time of image object identification, which transforms GEOBIA into an automated process. The proposed framework is applied to an urban case study on land use/land cover (LULC) from Hobart, Tasmania, Australia.

The traditionally human-visual or manual interpretation is generally chosen method for spatial feature extraction on map producing process in Indonesia, although the pixel-based feature extraction is used in particular step. The main problem of human-visual method is the dependency to operator capacity. Subjectivity may occur because each operator has different ability. In early 20th century, the image segmentation method was still developing and now Geographic Object-based Image Analysis (GEOBIA) method is introduced to mapping process. Researchers are still developing and applying GEOBIA for many mapping purposes. The advantage of GEOBIA is object-based segmentation which contextually influenced by spatial feature characteristics. GEOBIA provides more meaningful information than traditional pixel-based image analysis by allowing less well-defined edges or borders between different classes. On the resulting maps, there are divisions between different types of object classification, for example where some species of mangroves meet are not generally represented by a single object. The developing of remotely sensed imagery data quality requires the developing of extraction method as well. This article discusses the implementation of GEOBIA for coastal mapping in Indonesia. Some research combined pixel-based and GEOBIA method, and some get the overall accuracy > 70% of GEOBIA method. By developing a new segmentation accuracy measure, we evaluated that segmentation accuracies decrease with increasing segmentation scales and the negative impact of under-segmentation errors become significantly rise at large scale. The use of GEOBIA for coastal mapping in Indonesia are still lacking, locally applicable and still developing the transferability rule sets. The conclusion is that the innovation is needed to make GEOBIA method applicable for mass production of Indonesian coastal map.

Maize (Zea mays L.) kernel infection by Aspergillus flavus is a chronic problem in the southern USA. Genetic resistance to A. flavus is needed to solve this problem. To ascertain and understand the inheritance of resistance to field kernel infection by A. flavus, a five-parent diallel analysis and a half-sib family analysis of 35 maize crosses were conducted during 2003 and 2004 for percent kernel infection (PKI) rates. All parents contained the leafy gene (Lfy). From the combining ability analysis of the five maize parents and their 20 F1s, highly significant general combining ability (GCA), specific combining ability (SCA), and reciprocal effects were found. The analysis of genetic effects showed that the parents 914 and A619 had desirable GCA effects to enhance the average performance of A. flavus resistance in hybrid progeny. The crosses 914 �� A632, 914 �� WF9, and HY �� WF9 had consistently negative SCA effects across the two years. These results suggested that resistance to kernel infection by A. flavus existed among the parents and some of their crosses. Their potential performance with desired GCAs and SCAs could be exploited to develop resistant lines in breeding programs and to produce resistant hybrids. The reciprocal effects in the crosses across years reflected the presence of maternal effects in the maize kernel. These effects were partly responsible for resistance to A. flavus and should be considered in making crosses. The cross A632 �� HY had the highest negative significant reciprocal effect, indicating that it should promote resistance to A. flavus. Analyses of the 35 half-sib crosses derived from seven maize breeding lines indicated that both the genotype and genotype-by-year effects were highly significant. Broad-sense heritability for PKI estimated from variance components was 73.8%. A North Carolina Design-II analysis of 12 crosses was used to estimate additive and dominance genetic variances. Narrow-sense heritability and the average degree of dominance for PKI were 37.6% and 1.67, respectively. A comparison of a laboratory-based infection resistance screening (LIRS) with field-based PKI demonstrated that LIRS was effective and could be used to improve maize germplasm screening and to expedite A. flavus resistance breeding.

Anthocyanins in maize (Zea mays L.) kernels determine the plant's color and can enhance its resistance. Selenium (Se) significantly impacts plant growth, development, and secondary metabolic regulation. However, the molecular mechanisms by which Se regulates anthocyanin synthesis in waxy corn remain unclear. This study employed integrated transcriptomic and metabolomic analyses to investigate the mechanisms through which selenium influences anthocyanin synthesis in yellow and purple waxy corn. The results showed that maize varieties with higher anthocyanin content had higher selenium enrichment capacity in their kernels. Under selenium stress, HN2025 exhibited 1,904 more differentially expressed genes (DEGs) and 140 more differential metabolites compared to HN5. The expression levels of anthocyanin synthesis-related genes and transcription factors such as phenylalanine ammonia-lyase, flavonoid 3-hydroxylase (F3H), dihydroflavonol reductase (DFR), chalcone synthase (CHS), cinnamate-4-hydroxylase (C4H), anthocyanin 5,3-O-glucosyltransferases, and anthocyanidin reductase, MYB, and bHLH were strongly induced in HN2025. Metabolomic analysis revealed significant enrichment in anthocyanin biosynthesis, flavonoid and flavonol biosynthesis, glutathione metabolism, phenylalanine biosynthesis, and phenylalanine metabolism under selenium treatment. Three up-regulated PAL genes and one C4H gene were significantly enriched with DAMs in phenylalanine metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis, and anthocyanin biosynthesis, resulting in significant differences between HN5 and HN2025 in selenium-induced anthocyanin metabolism-related pathways. These findings provide a theoretical basis for understanding the effects of selenium on the molecular regulatory mechanisms of anthocyanin biosynthesis in maize kernels.

Geographic object-based image analysis (GEOBIA) is a promising methodology for image analysis, in which images are first segmented into image segments (or objects) and then analysed based on shape, texture, context and spectral features. The extra dimension of data offered by the objects yields a more enhanced image analysis. The first and most important step is thus the segmentation of images. The effectiveness of the object-based image analysis depends entirely on the quality of the segmentation result. There exist several types of image segmentation algorithms developed for a variety of applications ranging from medical imaging to remote sensing image analysis. It is, therefore, necessary to have an evaluation measure to decide which algorithm can be better for a particular task. Like segmentation itself, there is no standard way of evaluating segmentation results. In this article, we provide an easy way to analyse segmentation results by defining what qualifies as under-segmentation and over-segmentation while analysing the segmentation results of user-selected reference regions. The evaluation criteria are designed to handle the results of multi-level segmentation algorithms, which are commonly used in GEOBIA.

Accurate and reliable lithological mapping through satellite-borne remote sensing data and image classification approaches has a critical role since it can automatically and promptly identify lithological units over large areas. Most available Pixel-Object Based comparative classification studies have been applied to land use land cover (LULC) studies; however, this research aims to evaluate and compare the performance of these digital classification methods in the field of geological mapping in semi-arid areas, by integrating spectral bands and neo-bands, particularly the Minimum noise fraction (MNF) and the principal component analysis (PCA), of Sentinel-2A satellite imagery, to map the southern of Skhour Rehamna which is located at the western Moroccan Meseta. The analysis results from two different methods, namely, pixel-based image analysis (PBIA) with k-nearest neighbour (K-NN) and Random Forest (RF) machine learning algorithms (MLAs), and Geographic Object-Based Image Analysis (GEOBIA) were assessed and compared. PBIA method involved selection of training areas whether it was k-NN or RF MLAs, and produced lithological maps that exhibit “salt and pepper” effects as well as problems associated to delineating accurate lithological boundaries, while GEOBIA approach involved multi-resolution segmentation step where scale, shape and compactness parameters should be adjusted as accurate as possible, in order to segment the image into homogeneous and meaningful regions so that the resulted samples were classified using Standard Nearest Neighbour algorithm. Therefore, the resulting lithological maps were assessed by comparing both techniques using confusion matrix, overall accuracy (OA) and Kappa coefficient (K). The results show that the GEOBIA approach had higher overall agreement (83.46% OA and 0.76 K) than RF (81.92% OA and 0.72 K) and k-NN (80.79% OA and 0.70 K) PBIA approaches. Overall, the results clearly indicate the potential of GEOBIA technique for lithological mapping applications to produce more realistic maps.

The distribution of abscisic acid (ABA) within maize (Zea mays L.) kernels was studied in kernels from nontreated plants, from plants in which assimilate supply had been altered by source/sink manipulations, and in kernels cultured in vitro on ABA-free media. Prior to growth of the embryo, both the pedicel/placento-chalazal complex and the endosperm contained high concentration of ABA; however, the quantity of ABA in these tissues declined as the concentration in the embryo increased during the early stages of embryo growth. Peaks in the levels of ABA appeared to occur prior to and not concurrent with physiological events during grain filling. During most of the grain filling period, ABA concentration in the embryo was higher than that found in other kernel components. Altering assimilate supply by partial defoliation at two stages of development resulted in variable and transient effects on the relative distribution and concentration of ABA in kernel components. The concentration and distribution of ABA among components of kernels grown in vitro was similar to that observed for field-grown kernels. On the basis of these findings, in situ synthesis of ABA by kernel components is implicated and the putative role of ABA in the regulation of kernel development is discussed.

Maize plumpness believed to reflect yield and quality of maize products. Convenient and accurate methods may help identification of maize quality for produces and germplasm resources for breeding. In this study, the 3D reconstruction of maize kernel based on micro-CT technology was introduced to detect anatomical difference between diverse classes of maize kernel. Void spaces measurements constructed for whole maize (Zea mays L.) kernel gives a more accurate volume measurement for density calculations by means of a package of commercial softwares. Moreover, the ratio of cavity and porosity of the entire kernel were calculated based on the 3D CT images. Kernel density, cavities, porosity, and other phenotypic characteristics were closely related to seed plumpness classification. Compared with previous methods, our method significantly improves the calculation accuracy of kernel volume, cavity and porosity, and which is expected to be useful for efficient maize kernel plumpness classification.

To study the influence of PINFORMED1 (PIN1)-mediated auxin transport during embryogenesis and endosperm development in monocots, the expression pattern of the three identified ZmPIN1 genes was determined at the transcript level. Localization of the corresponding proteins was also analyzed during maize (Zea mays) kernel development. An anti-indole-3-acetic acid (IAA) monoclonal antibody was used to visualize IAA distribution and correlate the direction of auxin active transport, mediated by ZmPIN1 proteins, with the actual amount of auxin present in maize kernels at different developmental stages. ZmPIN1 genes are expressed in the endosperm soon after double fertilization occurs; however, unlike other tissues, the ZmPIN1 proteins were never polarly localized in the plasma membrane of endosperm cells. ZmPIN1 transcripts and proteins also colocalize in developing embryos, and the ZmPIN1 proteins are polarly localized in the embryo cell plasma membrane from the first developmental stages, indicating the existence of ZmPIN1-mediated auxin fluxes. Auxin distribution visualization indicates that the aleurone, the basal endosperm transfer layer, and the embryo-surrounding region accumulate free auxin, which also has a maximum in the kernel maternal chalaza. During embryogenesis, polar auxin transport always correlates with the differentiation of embryo tissues and the definition of the embryo organs. On the basis of these reports and of the observations on tissue differentiation and IAA distribution in defective endosperm-B18 mutant and in N-1-naphthylphthalamic acid-treated kernels, a model for ZmPIN1-mediated transport of auxin and the related auxin fluxes during maize kernel development is proposed. Common features between this model and the model previously proposed for Arabidopsis (Arabidopsis thaliana) are discussed.

Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA—similar to other emerging paradigms—lacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontology—as compared to the decision tree classification without using the ontology—yielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations.

Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services including shoreline erosion control, habitat provision, and water filtration. However, these systems face significant global change as a result of a combination of anthropogenic stressors like coastal development and environmental stressors such as sea-level rise and disease. Traditional intertidal habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are mapped in a way that is often insufficient to make informed management decisions. Unoccupied aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface model from the UAS imagery. These products were used in a geographic object-based image analysis (GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a more informed classification than traditional techniques by providing textural and geometric context to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA can differentiate intertidal habitats. This method allows for effective monitoring that can inform management and restoration efforts.

Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.

BackgroundMaize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding.ResultsIn this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis.ConclusionsOur results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection.