Leaf Categories Research Articles

Effect of Alternate Bearing Phenomenon and Boron Foliar Application on Nitrogen-15 Uptake, Translocation and Distribution in Mango Tree (cv. Zebda)

The objectives of this investigation are to study nitrogen uptake, translocation, accumulation and distribution in mango tree organs using labeled nitrogen (15N) and to understand the mechanism of boron action in increasing fruit yield in the off-year. A field experiment was conducted using fifteen-year-old mango trees (cv. Zebda) grown at Al Malak Valley Farm, El-Sharkeya Governorate-Egypt. Treatments included the application of (15NH4)2SO4, “in the on-year”, at a rate of 50 g nitrogen/tree through the stem injection technique. While boron was sprayed on the same trees “in the off-year” at the following rates: 0.0 (control), 250 and 500 mg·L–1. The authors hypothesize that boron and nitrogen act synergistically to increase mango fruit yield in the off-year. Results indicated that the highest 15N uptake and accumulation in the on and off-years was observed in the upper (young leaves). When boron was applied at 250 mg·L–1, in the off-year, the upper (young leaves) recorded the highest 15N uptake and accumulation (%15Ndff = 13.93) relative to the other two leaf categories and those of the on-year. In the on-year fruit accumulated higher 15N than leaf or bud. In the off-year, bud exhibited the highest 15N accumulation without boron application, while leaves exhibited the highest 15N with boron application. The highest %15Ndff in all tree organs was observed at 250 mg·L–1 boron rate. Boron increased nitrogen uptake, translocation and accumulation in mango tree organs. A synergistic relationship was observed between boron and nitrogen which led to an increase in fruit yield in the off-year.

Effective medical leaf identification using hybridization of GMM-CNN

Medical plants play a vital role in curing many diseases. These plants, along with their leaves, have medicinal values. If these leaves are identified appropriately, they can be chosen directly to have more significant relief from the disease. Therefore, the identification of these medical species is a challenging task. The ecologically motivated Convolutional Neural Networks (CNNs) have substantially contributed to computer visual research. This article introduces a unique approach to medical leaf identification based on the hybridization of the Gaussian mixture model and a Convolutional Neural Network (GMM-CNN). The experimentation is performed on the Flavia dataset and is carried out using benchmark evaluation metrics. The parameters like index volume, probability of random index, and global consistency error are evaluated. The Python simulation model is utilized for the evaluation of the proposed methodology. The hybrid technique combining GMM and CNN has considerable potential in medical leaf identification. The experimental findings indicate that the hybrid strategy exhibits superior performances. The methodology suggested in this study demonstrates exceptional levels of accuracy, precision, and recall when applied to a wide range of medical leaf categories. Moreover, integrating Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN) addresses concerns associated with a scarcity of training data by offering a more resilient structure for extracting features and performing classification. By combining the advantages of statistical modelling with deep learning, we develop a resilient and precise system that has the potential to enhance botanical research, medical diagnostics, and environmental monitoring applications.

Context Elements Taxonomy for Intelligent Transportation Systems

Design and development of context-aware Intelligent Transportation Systems (ITS) are not trivial due to the large number of possible context elements that may be relevant to the application and the lack of structured information to guide system designers in this task. This paper proposes that context elements with common characteristics can be grouped into categories, and these categories can be organized in a taxonomy. This taxonomy could help system designers with the task of modeling and developing new context-aware ITS. We performed a literature review of 68 articles describing 70 ITS applications with context-aware features to identify context elements used in this type of application. Furthermore, we also analyzed three commercial ITS applications. We used data collected from the analysis of these 73 projects to define the categories and identify their relationships. We propose a taxonomy with 79 categories, with 57 leaf categories (a category without children subcategories). We also performed two experiments to validate whether the exposure to this taxonomy could improve the quality of an ITS application during its design, with favorable results showing a 2.7 times increase in the average amount of relevant context elements used in the application. Finally, we compiled a knowledge base of which context element categories are used in the 73 analyzed projects. It is another companion information that can be used to help system designers. The proposed taxonomy of context element categories organizes the information of the context-aware ITS domain in a way that can ease the task of designing such systems and improve the usage of context-aware features. The overall methodology used in this work to create the taxonomy for the ITS domain could be applied to other popular domains of context-aware applications.

Phenotyping grapevine red blotch virus and grapevine leafroll-associated viruses before and after symptom expression through machine-learning analysis of hyperspectral images.

Grapevine leafroll-associated viruses (GLRaVs) and grapevine red blotch virus (GRBV) cause substantial economic losses and concern to North America's grape and wine industries. Fast and accurate identification of these two groups of viruses is key to informing disease management strategies and limiting their spread by insect vectors in the vineyard. Hyperspectral imaging offers new opportunities for virus disease scouting. Here we used two machine learning methods, i.e., Random Forest (RF) and 3D-Convolutional Neural Network (CNN), to identify and distinguish leaves from red blotch-infected vines, leafroll-infected vines, and vines co-infected with both viruses using spatiospectral information in the visible domain (510-710nm). We captured hyperspectral images of about 500 leaves from 250 vines at two sampling times during the growing season (a pre-symptomatic stage at veraison and a symptomatic stage at mid-ripening). Concurrently, viral infections were determined in leaf petioles by polymerase chain reaction (PCR) based assays using virus-specific primers and by visual assessment of disease symptoms. When binarily classifying infected vs. non-infected leaves, the CNN model reaches an overall maximum accuracy of 87% versus 82.8% for the RF model. Using the symptomatic dataset lowers the rate of false negatives. Based on a multiclass categorization of leaves, the CNN and RF models had a maximum accuracy of 77.7% and 76.9% (averaged across both healthy and infected leaf categories). Both CNN and RF outperformed visual assessment of symptoms by experts when using RGB segmented images. Interpretation of the RF data showed that the most important wavelengths were in the green, orange, and red subregions. While differentiation between plants co-infected with GLRaVs and GRBV proved to be relatively challenging, both models showed promising accuracies across infection categories.

Intra-category Aware Hierarchical Supervised Document Hashing

Document hashing is a powerful paradigm for document retrieval, which maps high-dimensional documents to compact hashing codes with preserving the similarity of original data. While fairly successful, the existing document hashing methods do not consider the relevance relationship among different documents from a category and the hierarchical relationship among categories. Intuitively, the intra-category relevance connects related concepts among different documents, which can supplement the omitted information for each document; meanwhile the hierarchical categories can help to identify whether mistakes occur in leaf categories or parent categories, which can be used to reduce the mistakes occurring in parent categories that are often more serious. Inspired by above intuitions, we propose a novel \textbf{I}ntra-category aware \textbf{H}ierarchical supervised \textbf{D}ocument \textbf{H}ashing, called IHDH. Specifically, IHDH is a binary autoencoder architecture equipped with two novel components: intra-category component and hierarchy component. The intra-category component exploits the difference among latent semantic representations of different documents from a category to supplement the omitted information for each document. The hierarchy component utilizes the hierarchical structure to transform the probabilities of leaf categories into the probabilities of parent categories by union operation, and then gives a further parent-level penalty to reduce the mistakes occurring in parent categories.

Eucalyptus bakeri: a potential source species for eucalyptus oil production in the subtropics

ABSTRACT There is a paucity of eucalypt species in tropical Australia that produce a foliar essential oil in high concentration and rich in 1,8-cineole, the sought-after component of medicinal eucalyptus oils. Eucalyptus bakeri, a mallee from the dry subtropical parts of northern New South Wales and Queensland, is an exception, with limited earlier studies reporting that this species produced foliar oil with commercial characteristics. The aim of this study was to determine the variation in foliar oil characteristics throughout the natural range of E. bakeri as a precursor to the more intensive field-testing of the species as a commercial source of eucalyptus oil. Mature leaves and, when available, coppice leaves for comparison were collected over three separate field trips in 2020 from individual trees representing each of 17 provenances throughout the natural range of E. bakeri in Queensland. Foliar oil concentrations on a fresh-weight basis throughout the provenances sampled were relatively high, averaging 2.3% for mature leaves and 2.8% for coppice leaves, with an individual tree high of 4.1% for both leaf categories. 1,8-Cineole was the dominant compound in all foliar oils sampled, averaging 89.3% (range 70.2–94.6%) in oils from mature leaves and 87.6% (range 79–92.5%) from coppice leaves. Sabinene levels, restricted to a maximum of 0.3% in some oil standards, averaged 0.3% (range trace – 1.5%) in oils from mature leaves and 0.4% (range trace – 0.7%) from coppice leaves. The high foliar oil concentrations and consistency in oil qualities mark this species as a potential candidate as a eucalyptus oil source species for the dry subtropics. Provenance/progeny trials are now needed to determine which provenances produce adequate growth rates and the silviculture required to manage the species as an oil-producing crop, as well as to investigate variations in oil characteristics, including sabinene levels, when all sources are grown at a common site.

Combined effects of freshwater salinization and leaf traits on litter decomposition

Freshwater salinization is a matter of major global concern due to its consequences on the aquatic biota and ecosystems functioning. Salt contamination negatively affects the leaf litter decomposition process, a key ecosystem-level process in forested streams that contributes to the recycling of nutrients and carbon storage. However, information on how additional factors may influence the magnitude of the response to salinization is scarce. In this microcosm study we assessed the importance of leaf (Castanea sativa; Quercus robur) traits, on aquatic hyphomycetes-mediated leaf litter decomposition and associated variables, in salt-contaminated (0, 1, 3 and 6 g/l NaCl) environments. The leaves were incubated individually, and in a mixture, for 28 days, under each tested salt concentration. Salinity depressed leaf mass loss, fungal biomass, respiration and sporulation rates, particularly at the highest salt concentration. Differences across leaf categories were observed in all parameters but fungal biomass, although the effects were not consistent across descriptors. All leaf categories responded with a similar intensity to salt contamination for all measured variables but sporulation rate. These results suggest that the deleterious influence of salt on litter decomposition occurs independently of the traits of the stream riparian subsidies.

E-Commerce Product Categorization via Machine Translation

E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on machine translation . In our approach, we translate a product’s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products.

CNN-RNN: a large-scale hierarchical image classification framework

Objects are often organized in a semantic hierarchy of categories, where fine-level categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.

Canopy height and N affect herbage accumulation and the relative contribution of leaf categories to photosynthesis of grazed brachiariagrass pastures

AbstractThe effects of three canopy heights (10, 25 and 40 cm) and two N rates (50 and 250 kg ha−1 year−1) on herbage accumulation (HA), participation of leaf categories in the leaf area index (LAI) and photosynthesis of grazed “Mulato II” brachiariagrass (Brachiaria brizantha × B. decumbens × B. ruziziensis) were investigated during two summer growing seasons in Piracicaba, São Paulo, Brazil. The HA and LAI increased linearly from 8560 to 13600 kg DM ha−1 year−1 and 2.3 to 5.5 m2 m−2, respectively, as canopy height increased. Mulato II brachiariagrass maintained at 10 cm canopy height showed greater proportion of young leaves, but was less productive than taller canopies. Taller canopies had greater proportion of mature leaves in the LAI as well as greater photosynthesis. The greater N rate contributed to increased LAI, leaf and canopy photosynthesis rates and HA but did not affect the LAI composition. The total leaf area is more important to HA than the proportion of young leaves in the LAI, as taller canopies were more productive, suggesting that maintaining Mulato II brachiariagrass at 25 or 40 cm is advantageous for this grass.

Multi-Spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

Evaluation of the impact of herbicides on maize was done through multi- spectral and multi-modal imaging and multi-spectral fluorescence imaging combined with statistical methods. Spectra containing 13 wavelengths ranging from 375 nm to 940 nm were derived from multi-spectral images in transmission, reflection and scattering mode and fluorescence images obtained using high-pass filters (F450 nm, F500 nm, F550 nm, F600 nm, F650 nm) on control maize samples and maize samples treated with Herbextra herbicide were used. The appearance of the spectra allowed us to characterize the effect of the herbicide on the maize pigment concentration. The fluorescence images allowed us to track the fate of absorbed energy and through PLS-DA and SVM-DA to discriminate the two leaf categories with very low error rates for the test, i.e. 4.9% and 2% respectively. The results of this technique can be used in the context of precision agriculture.

Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests

Chlorophyll content is among the most important indicators of plant photosynthetic activity and is increasingly being monitored through hyperspectral remote sensing. In this study, we have targeted the best hyperspectral indices for tracing chlorophyll content in cold temperate deciduous forests from a total of 86 published representative hyperspectral indices, including 71 reflectance-based indices, 10 derivative-based indices and five feature-based indices. These indices have been thoroughly evaluated using the datasets from a dominant species (Fagus crenata) in one alpine site along the Japanese sea coast (Naeba site) and another alpine site along the Pacific Coast (Nakagawane site) composed of 29 species other than beech. Evaluation results based on beech leaves from the Naeba site revealed that the DDn had the best performance (R2=0.57), and its robustness has also been confirmed from the dataset obtained from the Nakagawane site (R2=0.47) when all leaf types were dumped. However, the indices that performed best were different when leaf types were taken into account, for which CI (R2=0.73) and D2 (R2=0.71) were identified for sunlit and shaded leaves, respectively. Astonishingly, they performed poorly with the other leaf type (R2=0.23 for CI with shaded leaves and 0.15 for D2 with sunlit leaves), suggesting the sensitivity of indices to different leaf categories. The measurements obtained from Nakagawane were also adopted to validate them based on the ratio of performance to deviation (RPD). For the sunlit leaf-oriented indices, the CI good again; however, those of the shaded-oriented indices were poor, and their robustness was not confirmed. As a result, there are no indices that are applicable for quantifying the chlorophyll concentrations of shaded leaves from different species; hence, it is required to develop a generally applicable index to express the differences in leaf type in future works.

Refractive index and spectral reflection in three leaf categories of strawberry (Fragaria x ananassa Duch.)

A study was carried out to examine the refractive index (RI) and red (R), green (G) and blue (B) colour spectral reflection of strawberry (Fragaria x ananassa Duch.) leaves. Digital photographs and their respective refractive indices of thirty trifoliate leaves from three leaf categories, i.e. most recently mature trifoliate leaves (MRTL), old trifoliate leaves (OTL) and leaf spot disease-infected trifoliate leaves (LSTL) of strawberry cv. Chandler were analysed. The highest RI was found in OTL. The spectral reflection of R, G, B (R+G+B) and spectral indices of B/R, (R-B)/(R+B) and B/(R+G+B) were not significantly different (p Tropical Agricultural Research Vol. 25 (2): 261 – 265 (2014)

Antioxidant potential of curry (Murraya koenigii L.) and mint (Mentha spicata) leaf extracts and their effect on colour and oxidative stability of raw ground pork meat during refrigeration storage.

The aim of this study was to investigate the antioxidant activity of different solvent extracts of curry and mint leaf and their effect on colour and oxidative stability of raw ground pork meat stored at 4 ± 1°C. The results indicated that among the two individual leaf categories, the ethanol extract of curry leaf (EHEC) and the water extract of mint leaf (WEM) showed higher DPPH and ABTS(+) activity. EHEC also exhibited the highest total phenolic contents while these were the lowest for WEM. WEM showed the highest superoxide anionic scavenging activity (%). The pork meat samples treated with EHEC and WEM showed a decrease in the Hunter L- and a-values and a increase in b-value during storage at 4°C. However, the pH and TBARS values were higher in control samples irrespective of storage periods. In conclusion, EHEC and WEM have the potential to be used as natural antioxidants to minimise lipid oxidation of pork products.

Leaf consumption by Sesarma plicata in a mangrove forest at Jiulongjiang Estuary, China

The feeding ecology of Sesarma plicata (Grapsidae: Sesarminae), the most abundant crab species in a mangrove forest dominated by Kandelia candel at Jiulongjiang Estuary, China, was investigated through field and laboratory experiments. Feeding preference and consumption rates were determined on mature, senescent and decomposed leaves of Kandelia candel, Bruguiera gymnorrhiza and Aegiceras corniculatum. In the laboratory, S. plicata preferred leaves of K. candel over those of B. gymnorrhiza and A. corniculatum, and consumed significantly more decomposed leaves than mature and senescent ones, irrespective of crab size. Field experiments with limited power failed to reveal detectable species preferences despite more consumption of K. candel, but decomposed leaves of each species were again preferred. Leaf characteristics associated with preference changed with plant species and leaf state. Low tannins and high water content characterized the preference for decomposed state of leaves. Species preference was significantly and negatively related to crude fibers and C:N ratios for mature leaves, and crude fiber for senescent leaves, but significantly and positively related to water content for decomposed leaves. Leaf consumption rates averaged for all leaf categories from laboratory no-choice feeding experiments were 0.101, 0.055 and 0.017 gDW ind−1 d−1 for large, medium and small crabs, respectively. In this forest, mean density of S. plicata was 20.5 ind m−2 as assessed by a manual catching method. Leaf litter removal rate during neap tides by sesarmid crabs was about 1.33 gDW m−2 d−1 in April 2006. The leaves removed by crabs were grazed on the sediment surface or taken into crab burrows, shredded and stored before being eaten.

Endophytic and epiphytic phyllosphere fungi of red-osier dogwood (Cornus stolonifera) in British Columbia

Endophytic and epiphytic phyllosphere fungi associated with red-osier dogwood (Cornus stolonifera), a deciduous shrub, were examined in coastal British Columbia, Canada. Current-year shoots were divided into four types based on the absence or presence of inflorescence and secondary elongated shoots at the apex of primary shoots. Leaves on these shoots were then classified into six categories so as to examine the effect of flowering, secondary shoot elongation, and shoot order within currentyear shoots on the occurrence of phyllosphere fungi. Species composition of fungi was markedly different between the interior and surface of leaves, whereas it was relatively similar among the six leaf categories in the interior or on the surface. Frequencies of the eight major species were not different between leaves on flowering and nonflowering shoots. The frequency of Colletotrichum gloeosporioides in the leaf interior was greater on leaves on the primary shoots that elongated the secondary shoots than on those that did not, and was greater on leaves on the primary shoots than on those on the secondary shoots. On the other hand, secondary shoot elongation and shoot order had no effect on the frequencies of C. gloeosporioides and the other seven epiphytes on leaf surfaces.

Endophytic and epiphytic phyllosphere fungi of red-osier dogwood (<i>Cornus stolonifera</i>) in British Columbia

Endophytic and epiphytic phyllosphere fungi associated with red-osier dogwood (Cornus stolonifera), a deciduous shrub, were examined in coastal British Columbia, Canada. Current-year shoots were divided into four types based on the absence or presence of inflorescence and secondary elongated shoots at the apex of primary shoots. Leaves on these shoots were then classified into six categories so as to examine the effect of flowering, secondary shoot elongation, and shoot order within current year shoots on the occurrence of phyllosphere fungi. Species composition of fungi was markedly different between the interior and surface of leaves, whereas it was relatively similar among the six leaf categories in the interior or on the surface. Frequencies of the eight major species were not different between leaves on flowering and nonflowering shoots. The frequency of Colletotrichum gloeosporioides in the leaf interior was greater on leaves on the primary shoots that elongated the secondary shoots than on those that did not, and was greater on leaves on the primary shoots than on those on the secondary shoots. On the other hand, secondary shoot elongation and shoot order had no effect on the frequencies of C. gloeosporioides and the other seven epiphytes on leaf surfaces.

Within-plant distribution of twospotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae), on ivy geranium: development of a presence-absence sampling plan.

The twospotted spider mite, Tetranychus urticae Koch, is an important pest of ivy geranium and other ornamental plants. As a part of our long-term goal to develop an integrated crop management program for ivy geraniums, the focus of this study was to produce a reliable sampling method for T. urticae on this bedding plant. Within-plant mite distribution data from a greenhouse experiment were used to identify the young-fully-opened leaf as the sampling unit. We found that 53% of the mites on a plant are on the young-fully-opened leaves. On average 22, 37, and 41% of the leaves belonged to the young, young-fully-opened, and old leaf categories, respectively. We then developed a presence-absence sampling method for T. urticae in ivy geranium using generic Taylor's coefficients for this pest. We found the optimal binomial sample sizes for estimating populations of T. urticae at densities of between 0 and 3 mites/leaf to be quite large; therefore, we recommend the use of numerical sampling within this range of T. urticae densities. We also suggest that population estimates of T. urticae on ivy geranium be done based on mite density/unit area of greenhouse space, both for conventional greenhouse pest management, and for determining how many phytoseid predators to release when using biological control.

Hierarchical Categorization Based on k-Nearest Neighbor Approach for Web Site Classification

Automatic categorization is a viable method to deal with the scaling problem on the World Wide Web. In this paper, we consider how to apply general text categorization techniques to Web site classification tasks. Two relevant issues concern, first, the case where the classifying object is not one document, as with a home page, but a Web site, which is a collection of Web pages. Second, real world Web directories have a complex hierarchical structure, in which leaf and non-leaf categories are directly assigned to Web sites, unlike the hierarchical structure treated in most previous research. On the first issue, this paper proposes the use of Web pages linked by a home page in addition to the home page itself. To accomplish this, we propose a Web site classification method based on connectivity analysis, as well as content analysis of Web sites. On the second issue, the hierarchical structure of classifiers is transformed into a flattened structure, but the classifier for each category uses features of its next-of-kin categories to take advantage of the hierarchical relationship. In experiments on a Korean commercial Web directory, the proposed classification method achieved an amazing improvement of microaveraging breakeven point by 36.6%, compared with an ordinary classifier.

Impact of trichome density on the infestation of Aphis gossypii Glover and incidence of virus disease in ashgourd [Benincasa hispida (Thunb.) Cogn.

The resistance of ashgourd to the melon-cottonaphid, Aphis gossypii (Glover), and the incidence of aphid-transmitted viral diseases was investigated following recording of aphid numbers and trichome (plant hair) numbers per cm2 of leaf on four parts of the cucurbit, ashgourd, representing four genotypes (Local Sylhet, Local Round, High Female and CQ-10-90). The Local Sylhet genotype was found to have the highest trichome density compared with those of Local Round, High Female, and CQ-10-90 ashgourd genotypes, and was least infested by Aphis gossypii. The percentage of virus-infected plants was also found to be lowest on the Local Sylhet genotype. Aphid numbers were lowest on the terminal and young leaves of ashgourd compared with mature and senescent leaves, due to the higher trichome density in the former leaf categories. Trichome density was found to have a significant negative influence on the number of aphids. Trichomes in ashgourd play a major role by providing a first line defence in reducing aphid infestation, with a subsequent beneficial effect of reducing the incidence of virus.