Leaf Texture Research Articles

Exploring Genetic Variation and Trait Correlations in ‘Shiqian Taicha’ Tea Based on Comprehensive Analyses of Morphological and Biochemical Diversity

This study explores the genetic variation and trait correlations within a collection of 52 ‘Shiqian Taicha’ tea germplasms originating from Shiqian County, Guizhou Province, China. The ‘Shiqian Taicha’ landraces exhibit substantial genetic diversity and are characterized by unique morphological and biochemical attributes. The Shannon-Weaver diversity index (H’) values for the morphological traits ranged from 0.37 to 2.06, with an average value of 1.02, which suggests a greater level of genetic diversity than those of other domestic tea germplasms. The biochemical analysis demonstrated considerable variation, with coefficients of variation (CVs) for biochemical components ranging from 3.73 to 54.05% (mean CV: 20.35%). In addition, 25 germplasms had epigallocatechin-3-gallate (EGCG) contents ≥ 10%. The correlation analysis revealed significant associations between morphological traits and biochemical components, with particularly positive correlations between the leaf texture and key biochemical components such as total catechins (TC) and EGCG (p < 0.01). The clustering analysis, which was based on morphological and biochemical profiles, further categorized the tea germplasms into distinct groups. Notably, SWD-G, SLD005, SWXD005, and SWD004 were identified as superior germplasms with comprehensive biochemical qualities and high EGCG contents. These unique resources have potential for the cultivation of specific tea varieties with regional characteristics.

Evaluation of the Ornamental Potential of Basil Grown in Field in the Federal District

Basil is commonly known and cultivated as a spicy and aromatic plant. However, ornamental use is possible in landscape projects. The advancement of new technologies highlighted the production of ornamental plants in Brazil, and among these stands are basil, a species commonly known for its spicy and aromatic properties. Nonetheless, with the increase in the demand for plants with multiple functions in the ornamental plant market, more studies are needed to evaluate the potential of this species. This work evaluated phenotypic characteristics to promote studies and elaborate technical manuals on growing basil for ornamental use. Therefore, an experiment was carried out at the Agua Limpa Farm (FAL) of the University of Brasilia (UnB), located in the Federal District, Brazil. Two genotypes were cultivated, Folha Fina (thin-leaf) and Folha Larga (broad-leaf) from Topseed Garden®, and the following characteristics were evaluated: canopy shape, leaf texture, stem, inflorescence, flower and leaf color, and leaf gloss. The evaluated genotypes did not show significant differences in stem (green), inflorescence (green), and flower colors (white). However, they showed differences in leaf color, with dark green leaves and thin, light green leaves. Both basil genotypes (Ocimum basilicum L.) have ornamental potential due to their favorable phenotype characteristics.

Zero shot plant disease classification with semantic attributes

In the rapidly evolving field of plant disease detection, the number and complexity of crop diseases are increasing, made worse by factors like climate change. Addressing these challenges requires robust and efficient methodologies capable of early and accurate disease identification. This paper explores the integration of advanced deep learning techniques, including pre-trained models, zero-shot learning, and semantic attributes to enhance the effectiveness of plant disease detection systems. High level features extracted from the images by these pretrained models capture crucial patterns, while domain-specific semantic attributes, such as leaf texture and color variations, enhance the understanding. Incorporating zero-shot learning enables adaptation to new and unseen diseases using semantic descriptions. Experimental validation across diverse plant species and disease types underscores the approach’s reliability in real-world agricultural scenarios. Our approach has demonstrated superior performance with plant village dataset, showing a significant improvement in accuracy and generalization. These results underscore the potential of our method to revolutionize plant disease detection and management in agricultural practices.

Medicinal plant species detection by comparison review

The detection of therapeutic plant species is critical in today’s environment. Though many valuable medicinal plants are essential for survival, everyone needs Ready-to-use chemical medicines instead of ayurvedic ones. Because herbal medicines are prepared on time, they cannot be stored with preservatives like pharmaceutical ones. Also, identifying correct medicinal plants is essential; it can be achieved by classifying herbs by leaf shape, colour and texture of both sides using the established parameters of various methods. Recent utilization of traditional medicinal plants has increased, but identifying correct medicinal plants is more complicated. All herbal plants are natural resources that are reliable and healthy for consumption. Medicinal plants are used to treat heart problems, respiratory problems, reproductive problems, gastrointestinal problems, joint problems, skin illnesses, excretory problems, etc. Because of the necessity for mass manufacturing, identifying these plants is urgent. Detecting and classifying medicinal species is more critical to provide better treatment. Due to the deficiency of knowledgeable persons, effective detection and categorization of edible herbal plants are difficult. As a result, a completely computerized approach to identifying edible herbal plants is an essential key factor of the research. In this paper, various plant detection techniques like Convolution Neural Network (CNN), pertained models VGG16, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Multi-Layer Perception (MLP) are taken and compared, and then finally concluded with the best results based on accuracy rate.

Drought response of zoysiagrass with varying leaf texture under progressive deficit irrigation

Drought response of zoysiagrass with varying leaf texture under progressive deficit irrigation

Evaluation of Turfgrasses for Qualitative and Quantitative Traits in Varanasi Region of India

The present experiment was conducted at Horticulture Research Farm, Department of Horticulture, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi. This study was carried out to evaluate performance of eleven turfgrass genotypes for qualitative traits, quantitative traits and their genetic variability under same climatic condition. The experimental design was a Randomized Block Design (RBD) with three replications. Eleven genotypes consist of Bermuda grass, Centipede grass, Panama grass, Bargusto grass, Tiftdwarf 419 grass, Local grass, Palma grass, Bahia grass, Manila grass, Crow Foot grass and Tanu Variant grass. These were assessed for their suitability to be used as turfgrass under Varanasi region. All the grasses exhibited semi prostrate growth habit except local grass having upright growth habit. Panama and Local grass showed fine leaf texture whereas, Manila grass exhibited coarse texture. Dark green colour was observed in Local, Crow Foot and Panama grass whereas, Centipede grass showed light green colour. Local grass exhibited maximum canopy height, leaf length, leaf width, chlorophyll content, stolen internode diameter, culm length, shoot length and shoot diameter. The maximum genotypic (187.22) and phenotypic variance (197.80) was observed for parameters like fresh weight. Canopy height showed maximum phenotypic coefficient of variation (47.04) and genotypic coefficient of variation (46.16) over all the parameters. Genetic advance maximum (27.49) for parameter fresh weight and genetic advance as a percentage of mean was maximum also for fresh weight (93.30). The maximum heritability at broad sense was observed for the parameter dry weight (98.25).

Suzhi 5: A New Hybrid Zoysiagrass Cultivar with High Shoot Density and Fine Leaf Texture

Suzhi 5: A New Hybrid Zoysiagrass Cultivar with High Shoot Density and Fine Leaf Texture

Calcium fertilization on the Phalaenopsis ssp. cultivation (Orchidaceae)

The cultivation efficiency and quality improvement of Phalaenopsis sp. production through mineral nutrition has been widely studied. The objective of this study was to evaluate the effects of different concentrations of calcium (Ca) in the nutrient solution on the growth, nutrient uptake, and overall health of Phalaenopsis sp. Seedlings obtained from in vitro propagation at 6 months of age were fed biweekly with a nutrient solution suggested by Hoagland and Arnon with modified Ca concentrations of 0 mM, 1.25 mM, 2.5 mM, 5 mM, 7.5 mM, and 10 mM. After 24 months, characteristics such as number of leaves, width and length of the largest leaf, total leaf area, dry mass of leaves and roots, leaf texture, and number and dry mass of flowers were evaluated. Macronutrient content was determined from leaf dry mass. Substrate pH and electrical conductivity were also measured. Increasing Ca concentrations up to 6.25 mM improved leaf length, total leaf area, number of flowers and flower dry mass. Leaf texture and electrical conductivity of the substrate increased up to a Ca concentration of about 2.5 mM. Leaf nitrogen and phosphorus contents increased with higher Ca concentrations. Optimal Ca fertilization at 6.25 mM had positive effects on both vegetative and reproductive growth, with no visual symptoms of Ca deficiency observed during vegetative development. These results emphasize the importance of precise and tailored nutrient management, not only to avoid hidden deficiencies, but also to maximize the quality and commercial value of Phalaenopsis sp. plants.

Phenotypic analysis of mezcal agaves from the Central Valleys of Oaxaca

Objective: Mezcal agaves in the state of Oaxaca have a high economic value due to the demand for mezcal production; therefore, the purpose of this work was to assess and highlight the importance of morphological diversity within and among cultivated Agave species. Design/Methodology/Approach: A completely randomized experimental design with 25 treatments (populations) and 11 replicates (individuals) was implemented. The plant (individual) was the experimental unit and 19 morphological descriptors proposed by the National Seed Inspection and Certification Service (SNICS) were assessed. A total of 275 individuals of the Agave angustifolia, Agave karwinskii, Agave marmorata, Agave rhodacantha, Agave potatorum, Agave seemanniana, and Agave nussaviorum species were assessed using a multivariate analysis to determine their phenotypic variability and existing relationships. Results: The dendrograms for the Q-mode and R-mode were obtained by means of a cluster analysis, forming 4 groups based on the average linkage generated from the standardized BDM of the sampled species of the genus Agave. Four groups were formed using the k-means clustering method, in accordance with field observations and a review of the taxonomic bibliography. The first two principal components (PC) accounted for 66.4% of the total variation, according to the principal component analysis (PCA). For PC1 and PC2, the variables with the highest contribution were those related to leaf shape (Fh), size of the lateral spine (FEL), number of leaves (Nh), plant height (H), uniformity in the size of the lateral spines (UTE), and terminal spine shape (FET). Study Limitations/Implications: A comprehensive study requires taxonomic keys to identify species, subspecies, and even varieties of Agave. Additionally, molecular characterization is essential to understand the variability and phylogenetic relationships of these populations, subject to a phylogenetic analysis. Findings/Conclusions: Multivariate analysis techniques revealed that three species showed high phenotypic variability in the maturation stage under cultivation conditions. The A. potatorum and A. karwinskii species had a greater intra-population phenotypic variability, with significant differences within the same species. Agave marmorata showed no intra- or inter-population variability. Leaf texture (Txh) was the only variable that explained the variation within its group. This is a tall species whose diameter is larger than in the other species. The variables of the Mexican (Agave rhodacantha) group showed low correlation, as their behavior was highly dispersed. The variables obtained in the field from this group of populations must be meticulously assessed to identify the degree of correlation between the variables and to confirm the behavior of this group.

Salvia glabrifolia (Lamiaceae), a new species from Hunan, China

Based on molecular and morphological evidence, a new species of Salvia (Lamiaceae), Salvia glabrifolia D.G. Zhang & X.J. Long, from Hunan Province, China, is described and illustrated. Salvia glabrifolia is morphologically similar to S. cavaleriei var. simplicifolia, S. daiguii, and S. cavaleriei var. cavaleriei but is distinct by its glabrous stem, petioles, veins, inflorescence axis, leaf margin repand, and glossy and subleathery leaf texture. Maximum likelihood (ML) analyses of the whole plastome dataset strongly supported S. glabrifolia as a sister to the S. japonica-S. liguliloba clade. Moreover, the chromosome number of S. glabrifolia, 2n = 16, is the same as other species in S. chinensis group.

Plant Disease Detection

Abstract: This project addresses the critical challenge of plant disease detection in India, a nation where agriculture supports nearly 70% of the population. Traditional procedures for identifying plant diseases are both labor-intensive and time-consuming. To enhance efficiency and accuracy, this project introduces an innovative approach that integrates machine learning and image processing techniques. By analyzing images of leaves, the system identifies diseases based on key factors such as leaf color, damage extent, area, and texture parameters. Unlike conventional methods that depend on visual inspections or chemical processes, this automated system provides a cost-effective and efficient solution for monitoring extensive crop fields. By leveraging statistical machine learning and advanced image processing algorithms, the proposed solution ensures precise disease detection with minimized computational complexity and reduced prediction time

Biomass Waste Utilization as Nanocomposite Anodes through Conductive Polymers Strengthened SiO2/C from Streblus asper Leaves for Sustainable Energy Storages.

Sustainable anode materials, including natural silica and biomass-derived carbon materials, are gaining increasing attention in emerging energy storage applications. In this research, we highlighted a silica/carbon (SiO2/C) derived from Streblus asper leaf wastes using a simple method. Dried Streblus asper leaves, which have plenty of biomass in Thailand, have a unique leaf texture due to their high SiO2 content. We can convert these worthless leaves into SiO2/C nanocomposites in one step, producing eco-materials with distinctive microstructures that influence electrochemical energy storage performance. Through nanostructured design, SiO2/C is thoroughly covered by a well-connected framework of conductive hybrid polymers based on the sodium alginate-polypyrrole (SA-PPy) network, exhibiting impressive morphology and performance. In addition, an excellent electrically conductive SA-PPy network binds to the SiO2/C particle surface through crosslinker bonding, creating a flexible porous space that effectively facilitates the SiO2 large volume expansion. At a current density of 0.3 C, this synthesized SA-PPy@Nano-SiO2/C anode provides a high specific capacity of 756 mAh g-1 over 350 cycles, accounting for 99.7% of the theoretical specific capacity. At the high current of 1 C (758 mA g-1), a superior sustained cycle life of over 500 cycles was evidenced, with over 93% capacity retention. The research also highlighted the potential for this approach to be scaled up for commercial production, which could have a significant impact on the sustainability of the lithium-ion battery industry. Overall, the development of green nanocomposites along with polymers having a distinctive structure is an exciting area of research that has the potential to address some of the key challenges associated with lithium-ion batteries, such as capacity degradation and safety concerns, while also promoting sustainability and reducing environmental impact.

Medicinal Plant Identification using Machine Learning

Medicinal plants play a crucial role in traditional and modern medicine due to their properties. In this study, we propose the identification of medicinal plants using machine learning. The proposed system comprises several key components: data collection, feature extraction, model training, and prediction. Initially, a comprehensive dataset of medicinal plant images is gathered from databases. Next, relevant features such as leaf shape, texture, and colour are extracted from images using image processing techniques. Subsequently, ML algorithms such as Convolutional Neural Networks (CNNs) are employed to train a classification model on the extracted features. The trained model is then capable of accurately identifying medicinal plants from input images. To facilitate user interaction, a user-friendly interface may be developed, allowing users to upload images and receive instant identification results. Furthermore, the system's performance is evaluated in real-world scenarios to assess its practical utility and reliability. Overall, the proposed automated medicinal plant identification system represents a significant advancement in leveraging ML technology to streamline the identification process

Gastropod grazing on fresh and senescent leaves of non-native invasive plants Reynoutria japonica and Impatiensglandulifera

In invaded regions, the introduced non-native plants Reynoutria japonica and Impatiens glandulifera show little grazing damage, most likely due to the absence of specialized herbivores and the deterrent effect of secondary metabolites on generalist herbivores. The low degree of grazing damage has been explained by the non-mutually exclusive enemy-release and novel weapon hypotheses. We tested assumptions of these hypotheses by conducting a series of preference tests in which leaf samples from R. japonica, I. glandulifera, and the native Urtica dioica were offered to five species of generalist gastropods (the native Arianta arbustorum, Cepaea nemoralis and Fruticicola fruticum, and the non-native invasive Hygromia cinctella and Arion vulgaris). In addition, we determined the C/N-ratio and total phenolic compounds (as a surrogate of secondary metabolites) of the plant species. In the choice experiment with fresh leaf samples, all snail species showed a preference for U. dioica, with the exception of the non-native invasive slug A. vulgaris, which ate leaf tissue from I. gladulifera almost as much as from U. dioica. The snails’ preference of U. dioica was even more pronounced when the fresh weight of leaf material eaten was considered. No-choice tests with either fresh or dead R. japonica leaves showed that most individuals of all species ate small amounts of fresh leaves, but less of dead leaves. In contrast, no-choice tests with either fresh or senescent I. glandulifera leaf tissue showed that individuals of all five gastropod species consumed larger amounts of senescent leaves than fresh leaves, probably because secondary compounds have been broken down or leached or the leaf texture has changed. The low susceptibility of these non-native invasive plants to the gastropods can most likely be explained by a combination of chemical and physical plant characteristics.

Plant leaf vein and outline feature extraction using fractal and computer vision approaches

The study focuses on utilizing plant leaf characteristics for plant identification and disease detection. Leaves are pivotal for gathering information about plants. Leveraging computer vision and smart agricultural technologies, the proposed model discerns venation and texture features in various plant leaves. This research utilized a modified dataset derived from the Flavia leaf image dataset, comprising images of 32 different plant species. The dataset was divided into two subsets (one with 1907 images and another with 1000 images) to differentiate between tuned and untuned image processing. Techniques such as GLCM, LBP, Gabor filters, Fractal Dimension, and box counting were employed to extract leaf texture features, including venation patterns. The study conducted four experiments with training and testing splits of 70/30 and 80/20. A novel method combining SVM with fractal dimension analysis was benchmarked against six classifiers (Random Forest, KNN, DNN, Naïve Bayes, Decision Tree, and SVM), achieving an impressive accuracy of 88% and a Fractal Dimension of 1.8709. This research holds significant potential for advancing digital and modern agriculture, particularly in the early detection of plant diseases and accurate plant identification.

The Effects of Different Nitrogenous Fertilizer Sources and Doses on Football Field Grass Performances

Nitrogen has a huge importance in terms of ensuring the development of roots and shoots in grass and giving plants resistance to diseases. Using the right fertilizer in appropriate doses in the maintenance and repair of football fields is one of the maintenance procedures that increase grass performance. This study was carried out on the football field grass established at Iğdır University Sehit Bulent Yurtseven Campus during the 2020 vegetation period in Iğdır conditions. The research aimed to determine the most suitable fertilizer type and dose for football fields. The experiment was designed with three replications according to the randomized blocks factorial trial design. In the application area, a grass mixture of Festuca arundinacea Jaguar 4G 30%, Festuca arundinacea Apache 20%, Festuca arundinacea Arid III 20%, Lolium perenne Belida 15% and Lolium perenne Esquire 15% was used. As nitrogenous fertilizer sources, 20-10-10 7SO3, 15-5-20 + 2 CaO + MgO, ammonium sulfate (21% N) and urea (46% N) fertilizers are 0, 2, 4, 6 and 8 g/m 2/ It was administered in monthly doses. In the study, the effects of nitrogen use in fertilization on grass plant height, quality, fresh grass amount, and leaf green tone and leaf texture were observed. As a result of the study, it was determined that the most ideal fertilizer type and dose were 6 and 8 g m-2 doses of urea and ammonium sulfate fertilizers.

Melanoserispenghuana (Lactucinae, Cichorieae, Asteraceae), a new species from North-central Yunnan, China.

In this paper, we describe a new species, Melanoserispenghuana, from Mt. Jiaozi Xueshan located in North-central Yunnan, China. Despite its morphological similarities to M.likiangensis, M.penghuana exhibits distinct differences in leaf texture, shape of terminal lobes, indumentum of leaves, peduncles, and involucres, as well as the length of the achenes. Additionally, the conservation status of this species is classified as Vulnerable through data analysis from two field surveys.

Diversidade genética de mangostão cultivado e seus parentes silvestres (Garcinia spp.) com base na morfologia foliar e marcadores moleculares

ABSTRACT The mangosteen (Garcinia mangostana L.) germplasm still has limitations in fruit quality, drought tolerance and susceptibility to pests or diseases. This study investigated the genetic diversity and relationships of mangosteen with its wild relatives (Garcinia spp.) based on leaf morphology and the internal transcribed spacer (ITS) region, including its secondary structure. Based on leaf morphology, the mangosteen and its wild relatives generally showed a low genetic diversity. However, the leaf texture and pubescence had a high genetic diversity (0.71 and 0.77, respectively). Furthermore, based on the ITS markers, the genetic diversity of Garcinia at the interspecies level was much higher than that at the intraspecies one (0.043 and 0.005, respectively). The unweighted pair group method with the arithmetic average (UPGMA) revealed that mangosteen is grouped into four main clusters, with ‘Manggis Banjar’ and ‘Palembang’ in the same cluster. Similarly, the ITS positioned Garcinia into several clades, with ‘Manggis Banjar’, ‘Kandangan’ and ‘Palembang’ grouped into a similar clade. The biochemical reconstruction showed that Garcinia has unique ITS secondary structures, i.e., ring and four-helix models. Even though the cultivated mangosteen and its wild relatives had low diversity based on leaf morphology, the ITS markers showed a high genetic diversity. Furthermore, the reconstruction of the ITS secondary structure has supported this germplasm’s phylogenetic tree.

Application of Transfer Learning in Smart Agriculture to Combat Black Rot Bacteria

With diseases and weeds alone accounting for approximately 30% of productivity losses in agriculture worldwide, plant diseases are a significant factor. Farmers frequently consult experts to diagnose their crops in an effort to reduce these losses. On the other hand, conventional visual assessment techniques focused on elements like leaf color, shape, and texture can be inefficient, time-consuming, and expensive. This study suggests using symptom traits like yellow leaves, black lesions, wilting, and plant mortality in crops like apple and grape to anticipate plant diseases, notably black rot. AlexNet, VGGNet, ResNet, and Inception are the four deep learning models examined in the study; VGGNet achieved the greatest accuracy of 98.19%.

Early Detection of Dicamba and 2,4-D Herbicide Drifting Injuries on Soybean with a New Spatial–Spectral Algorithm Based on LeafSpec, an Accurate Touch-Based Hyperspectral Leaf Scanner

In soybeans, off-target damage from the use of dicamba and 2,4-D herbicides for broadleaf weed control can significantly impact sensitive vegetation and crops. The early detection and assessment of such damage are critical for plant diagnostic labs and regulatory agencies to inform regulated usage policies. However, the existing technologies that calculate the average spectrum often struggle to detect and differentiate the damage caused by these herbicides, as they share a similar mode-of-action. In this study, a high-precision spatial and spectral imaging solution was tested for the early detection of dicamba and 2,4-D-induced damage in soybeans. A 2021 study was conducted using LeafSpec, a touch-based hyperspectral leaf scanner, to detect damage on soybean leaves. VIS-NIR (visible–near infrared) hyperspectral images were captured from 180 soybean plants exposed to nine different herbicide treatments at different intervals after spraying. Leaf damage was distinguished as early as 2 h after treatment (HAT) using pairwise partial least squares discriminant analysis (PLS-DA) models based on spectral data. Leaf color distribution, texture, and morphological features were analyzed to separate herbicide dosages. By fully exploiting the spatial and spectral information from high-resolution hyperspectral images, classification accuracy was improved from 57.4% to over 80% for all evaluation dates. This work demonstrates the potential and advantages of using spectral and spatial features of LeafSpec hyperspectral images for the early and accurate detection of herbicide damage in soybean plants.