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30 pages, 15808 KiB  
Article
Exploring the Streetscape Perceptions from the Perspective of Salient Landscape Element Combination: An Interpretable Machine Learning Approach for Optimizing Visual Quality of Streetscapes
by Wanyue Suo and Jing Zhao
Land 2025, 14(7), 1408; https://doi.org/10.3390/land14071408 - 4 Jul 2025
Viewed by 446
Abstract
Understanding how people perceive urban streetscapes is essential for enhancing the visual quality of the urban environment and optimizing street space design. While perceptions are shaped by the interplay of multiple visual elements, existing studies often isolate single semantic features, overlooking their combinations. [...] Read more.
Understanding how people perceive urban streetscapes is essential for enhancing the visual quality of the urban environment and optimizing street space design. While perceptions are shaped by the interplay of multiple visual elements, existing studies often isolate single semantic features, overlooking their combinations. This study proposes a Landscape Element Combination Extraction Method (SLECEM), which integrates the UniSal saliency detection model and semantic segmentation to identify landscape combinations that play a dominant role in human perceptions of streetscapes. Using street view images (SVIs) from the central area of Futian District, Shenzhen, China, we further construct a multi-dimensional feature–perception coupling analysis framework. The key findings are as follows: 1. Both low-level visual features (e.g., color, contrast, fractal dimension) and high-level semantic features (e.g., tree, sky, and building proportions) significantly influence streetscape perceptions, with strong nonlinear effects from the latter. 2. K-Means clustering of salient landscape element combinations reveals six distinct streetscape types and perception patterns. 3. Combinations of landscape features better reflect holistic human perception than single variables. 4. Tailored urban design strategies are proposed for different streetscape perception goals (e.g., beauty, safety, and liveliness). Overall, this study deepens the understanding of streetscape perception mechanisms and proposes a highly operational quantitative framework, offering systematic theoretical guidance and methodological tools to enhance the responsiveness and sustainability of urban streetscapes. Full article
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19 pages, 2755 KiB  
Article
Real-Time Algal Monitoring Using Novel Machine Learning Approaches
by Seyit Uguz, Yavuz Selim Sahin, Pradeep Kumar, Xufei Yang and Gary Anderson
Big Data Cogn. Comput. 2025, 9(6), 153; https://doi.org/10.3390/bdcc9060153 - 9 Jun 2025
Cited by 2 | Viewed by 856
Abstract
Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome [...] Read more.
Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R2 = 0.77), while RF demonstrated superior performance for dry biomass estimation (R2 = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation. Full article
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13 pages, 1376 KiB  
Article
Combining Organic and Foliar Fertilization to Enhance Soil Fertility and Mitigate Physiological Disorders of Durian (Durio zibethinus Murr.) Fruit in the Tropics
by Le Van Dang, Nguyen Kim Quyen, Ngo Phuong Ngoc, Le Minh Ly, Pham Thi Phuong Thao and Ngo Ngoc Hung
Plants 2025, 14(8), 1185; https://doi.org/10.3390/plants14081185 - 11 Apr 2025
Viewed by 1433
Abstract
Physiological disorders (PDs) in durian lead to reduced commodity prices; therefore, reducing the PD rate in durian enhances the fruit’s value and farmers’ profits. Nutrient and soil management may affect the PD rate during fruit development. Herein, we used amendments such as organic [...] Read more.
Physiological disorders (PDs) in durian lead to reduced commodity prices; therefore, reducing the PD rate in durian enhances the fruit’s value and farmers’ profits. Nutrient and soil management may affect the PD rate during fruit development. Herein, we used amendments such as organic manure (OM) and foliar fertilization (FF) applications to reduce the PD rate and improve the soil health and fruit yield of durian. This study was conducted in three durian orchards in the Vietnamese Mekong Delta from 2022 to 2024. The treatments were as follows: (i) control (unamended), (ii) OM, (iii) FF, and (iv) OM + FF. N−P−K fertilizers (0.45 kg of N, 0.45 kg of P, and 0.45 kg of K per tree) were uniformly applied to all durian trees. We measured the characteristics of the soil, such as the soil pH, soil organic carbon (SOC), available phosphorus (AP), and exchangeable cations (K+ and Ca2+). The leaf nutrient (K and Ca) content, fruit yield (kg tree−1), and fruit quality (PD rate, total soluble solids (TSS), and aril color characteristics) were also recorded. Our study indicates that OM + FF increased soil pH and SOC, AP, and exchangeable cations (K+ and Ca2+). In addition, the K and Ca concentrations in durian leaves increased by approximately 4% using OM + FF. Combining OM and FF decreased the PD rate of durian fruit (>85%) compared with the control. This practice increased the fruit quality TSS (13%), color, proportion of arils, and fruit yield (~10%) compared with conventional practice (control). Overall, using OM and FF contributed to improving durian production and values. Therefore, we recommend that farmers who cultivate durian apply OM + FF to their orchards to enhance soil health, fruit quality, and yield. Full article
(This article belongs to the Section Crop Physiology and Crop Production)
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17 pages, 10450 KiB  
Article
Development of a High-Efficiency, Tissue Culture-Independent Genetic Transformation System for Loropetalum chinense
by Tingting Li, Yi Yang, Yang Liu, Wei Tang, Yang Liu, Damao Zhang, Chengcheng Xu, Xingyao Xiong, Xiaoying Yu and Yanlin Li
Horticulturae 2025, 11(4), 404; https://doi.org/10.3390/horticulturae11040404 - 10 Apr 2025
Viewed by 535
Abstract
Loropetalum chinense is a significant small tree and ornamental shrub known for its colorful foliage and is widely used in landscaping in tropical and subtropical regions. This study aimed to establish an efficient, tissue culture-independent genetic transformation system for L. chinense. Cuttings [...] Read more.
Loropetalum chinense is a significant small tree and ornamental shrub known for its colorful foliage and is widely used in landscaping in tropical and subtropical regions. This study aimed to establish an efficient, tissue culture-independent genetic transformation system for L. chinense. Cuttings from two varieties, ‘Xiangnong Xiangyun’ and ‘Hei Zhenzhu’, were infected with different strains of Agrobacterium rhizogenes. The results showed that the K599 strain significantly induced hairy roots in both varieties, with ‘Xiangnong Xiangyun’ demonstrating a higher survival rate (60%), rooting rate (51.66%), and hairy root induction efficiency (45%) compared to ‘Hei Zhenzhu’. Based on these findings, ‘Xiangnong Xiangyun’ and the K599 strain were selected for further optimization through an orthogonal L9 (33) experiment, which focused on optimizing the infection solution composition, bacterial concentration, and infection duration, Finally, the genetic transformation system established at the beginning of the experiment was validated on ‘Xiangnong Xiangyun’ plants using the pre-screening LcDREB-43 gene of our group. Among these factors, infection duration was identified as the most influential for improving transformation efficiency. The optimal conditions were determined as an infection solution containing MES solution, a bacterial concentration of OD600 = 0.8, and a 15 min infection duration. Under these optimized conditions, the survival rate, rooting rate, induction efficiency, and transformation efficiency reached 86.67%, 70%, 61.67%, and 43.33%, respectively. Furthermore, the transgenic plants with LcDREB-43 overexpression and pCAMBIA1305-GFP were obtained through the established transformation system, the authenticity of the system was proved, and the production application was carried out through phenotypic observation, molecular identification, and auxiliary verification of physiological indicators. Full article
(This article belongs to the Section Propagation and Seeds)
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14 pages, 4023 KiB  
Article
Characterization of the Complete Chloroplast Genomes and Phylogenetic Analysis of Sapotaceae
by Wenyan He, Yumei Liu, Rui Gao, Zhiyu Song, Wentao Zhu, Jinliao Chen, Cuiyi Liang, Shasha Wu and Junwen Zhai
Horticulturae 2024, 10(12), 1375; https://doi.org/10.3390/horticulturae10121375 - 20 Dec 2024
Viewed by 803
Abstract
The Sapotaceae family comprises 65–70 genera and over 1250 species, holding significant ecological and economic value. Although previous studies have made some progress in the phylogenetic relationships and classification of Sapotaceae, many issues remain unresolved and require further in-depth research. In this study, [...] Read more.
The Sapotaceae family comprises 65–70 genera and over 1250 species, holding significant ecological and economic value. Although previous studies have made some progress in the phylogenetic relationships and classification of Sapotaceae, many issues remain unresolved and require further in-depth research. In this study, we sequenced and assembled the complete chloroplast genomes of 21 plants from 11 genera of Sapotaceae, conducted a comparative genomic analysis, and performed a phylogenetic analysis by incorporating 16 previously published chloroplast genomes of Sapotaceae. The results showed that the chloroplast genome sizes in 21 plants of Sapotaceae range between 157,920 bp and 160,130 bp, exhibiting the typical quadripartite structure. Each genome contains 84–85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes, while the ndhF gene is absent in Pouteria campechiana and Pouteria sapota. The relative synonymous codon usage (RSCU) analysis showed that isoleucine (Ile) is the most commonly used, while the codon for methionine (Met) is the least utilized. Additionally, five highly variable regions (petA-psbJ, psbI-trnS-GGA, rpl2_2-psbA, rps19-rpl2_2, and ycf4-cemA) and two coding sequences, ycf1 and matK, were identified as candidate molecular markers for species differentiation and a phylogenetic analysis within the Sapotaceae family. Phylogenetic trees were reconstructed using complete chloroplast genome sequences and analyzed using ML and BI methods, which revealed that the Sapotaceae family is divided into three distinct clades, each receiving strong statistical support (BS = 100, PP = 1). The intergeneric analysis revealed that Madhuca and Palaquium are sister groups (BS = 91, PP = 1), as are Gambeya and Chrysophyllum (BS = 91, PP = 1). Pouteria and Chrysophyllum are among the larger groups in the Sapotaceae family but the traditional classification boundaries of these genera are unstable and unfeasible, as the current genus boundaries fail to support their natural evolutionary relationships. In the phylogenetic tree, Eberhardtia aurata is placed on a separate branch. The morphological classification system shows that E. aurata has rust-colored pubescence on its branches, abaxial leaf surfaces, petioles, and other areas, which clearly distinguishes it from other genera. This study provides valuable insights into advancing phylogenetic research, population genetics, molecular breeding, and conservation strategies by comparing chloroplast genome structures and characteristics and constructing phylogenetic trees. Full article
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20 pages, 10618 KiB  
Article
Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding Sunflowers
by Shuailing Zhang, Hailin Yu, Bingquan Tian, Xiaoli Wang, Wenhao Cui, Lei Yang, Jingqian Li, Huihui Gong, Junsheng Zhao, Liqun Lu, Jing Zhao and Yubin Lan
Agronomy 2024, 14(10), 2205; https://doi.org/10.3390/agronomy14102205 - 25 Sep 2024
Viewed by 1249
Abstract
In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned [...] Read more.
In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R2 is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding. Full article
(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture—2nd Edition)
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20 pages, 1817 KiB  
Article
Optimizing Sweet Cherry Attributes through Magnesium and Potassium Fertilization
by Marlene Santos, Sandra Pereira, Helena Ferreira, João Ricardo Sousa, Alice Vilela, Carlos Ribeiro, Fernando Raimundo, Marcos Egea-Cortines, Manuela Matos and Berta Gonçalves
Horticulturae 2024, 10(8), 881; https://doi.org/10.3390/horticulturae10080881 - 20 Aug 2024
Cited by 5 | Viewed by 1413
Abstract
Plant nutrition through fertilizer application plays a crucial role in enhancing crop quality and yield, necessitating a balanced fertilization approach. Sweet cherry, esteemed as one of the most prized crops worldwide, was the focus of this three-year study spanning from 2019 to 2021, [...] Read more.
Plant nutrition through fertilizer application plays a crucial role in enhancing crop quality and yield, necessitating a balanced fertilization approach. Sweet cherry, esteemed as one of the most prized crops worldwide, was the focus of this three-year study spanning from 2019 to 2021, involving the sweet cherry cultivar Burlat. This study investigated the foliar application of magnesium (Mg) and potassium (K) to enhance fruit quality parameters. Different doses of Mg (250 g hL−1 and 125 g hL−1) and K (100 g hL−1 and 50 g hL−1) and a control treatment were administered to sweet cherry trees to assess their impact on fruit quality. At the commercial ripening stage, fruits from each treatment were harvested for comprehensive evaluation, including biometric and chromatic parameters, fruit firmness, routine parameters, sensory profile, and nutrient content analysis. Results from the study revealed notable enhancements in fruit weight and dimensions, particularly in the control treatment in 2020. Furthermore, Mg125 and Mg250 treatments exhibited improved color development and accelerated maturity by increasing the total soluble solids content while decreasing titratable acidity. Sensorial profiling indicated that Mg125 and Mg250 treatments intensified color intensity and sweet taste while mitigating sour taste perceptions. Conversely, potassium fertilization, especially the K50 treatment, led to increased fruit firmness and nutrient content. These findings offer valuable insights into optimizing sweet cherry production practices globally. Full article
(This article belongs to the Section Fruit Production Systems)
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19 pages, 8831 KiB  
Article
Tongue Disease Prediction Based on Machine Learning Algorithms
by Ali Raad Hassoon, Ali Al-Naji, Ghaidaa A. Khalid and Javaan Chahl
Technologies 2024, 12(7), 97; https://doi.org/10.3390/technologies12070097 - 28 Jun 2024
Cited by 8 | Viewed by 19646
Abstract
The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels [...] Read more.
The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems. Full article
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11 pages, 5056 KiB  
Article
Nighttime Harvesting of OrBot (Orchard RoBot)
by Jakob Waltman, Ethan Buchanan and Duke M. Bulanon
AgriEngineering 2024, 6(2), 1266-1276; https://doi.org/10.3390/agriengineering6020072 - 8 May 2024
Viewed by 2109
Abstract
The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and [...] Read more.
The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 s. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted in images with the lowest noise. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94% compared to 88% during the daytime operations. Full article
(This article belongs to the Section Sensors Technology and Precision Agriculture)
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24 pages, 2940 KiB  
Article
DNA Barcoding and Fertilization Strategies in Sideritis syriaca subsp. syriaca, a Local Endemic Plant of Crete with High Medicinal Value
by Konstantinos Paschalidis, Dimitrios Fanourakis, Georgios Tsaniklidis, Ioannis Tsichlas, Vasileios A. Tzanakakis, Fotis Bilias, Eftihia Samara, Ioannis Ipsilantis, Katerina Grigoriadou, Ioulietta Samartza, Theodora Matsi, Georgios Tsoktouridis and Nikos Krigas
Int. J. Mol. Sci. 2024, 25(3), 1891; https://doi.org/10.3390/ijms25031891 - 4 Feb 2024
Cited by 4 | Viewed by 3118
Abstract
Herein, we applied DNA barcoding for the genetic characterization of Sideritis syriaca subsp. syriaca (Lamiaceae; threatened local Cretan endemic plant) using seven molecular markers of cpDNA. Five fertilization schemes were evaluated comparatively in a pilot cultivation in Crete. Conventional inorganic fertilizers (ChFs), integrated [...] Read more.
Herein, we applied DNA barcoding for the genetic characterization of Sideritis syriaca subsp. syriaca (Lamiaceae; threatened local Cretan endemic plant) using seven molecular markers of cpDNA. Five fertilization schemes were evaluated comparatively in a pilot cultivation in Crete. Conventional inorganic fertilizers (ChFs), integrated nutrient management (INM) fertilizers, and two biostimulants were utilized (foliar and soil application). Plant growth, leaf chlorophyll fluorescence, and color were assessed and leaf content of chlorophyll, key antioxidants (carotenoids, flavonoids, phenols), and nutrients were evaluated. Fertilization schemes induced distinct differences in leaf shape, altering quality characteristics. INM-foliar and ChF-soil application promoted yield, without affecting tissue water content or biomass partitioning to inflorescences. ChF-foliar application was the most stimulatory treatment when the primary target was enhanced antioxidant contents while INM-biostimulant was the least effective one. However, when the primary target is yield, INM, especially by foliar application, and ChF, by soil application, ought to be employed. New DNA sequence datasets for the plastid regions of petB/petD, rpoC1, psbK-psbI, and atpF/atpH were deposited in the GenBank for S. syriaca subsp. syriaca while the molecular markers rbcL, trnL/trnF, and psbA/trnH were compared to those of another 15 Sideritis species retrieved from the GenBank, constructing a phylogenetic tree to show their genetic relatedness. Full article
(This article belongs to the Collection Feature Papers in Molecular Plant Sciences)
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13 pages, 2927 KiB  
Article
Positive Changes in Fruit Quality, Leaf Antioxidant Defense System, and Soil Fertility of Beni-Madonna Tangor Citrus (Citrus nanko × C. amakusa) after Field AMF Inoculation
by Li-Jun Zhou, Yu Wang, Mashael Daghash Alqahtani and Qiang-Sheng Wu
Horticulturae 2023, 9(12), 1324; https://doi.org/10.3390/horticulturae9121324 - 9 Dec 2023
Cited by 4 | Viewed by 1859
Abstract
Citrus plants rely heavily on arbuscular mycorrhizal fungi (AMF) due to their lack of root hairs. Most experiments have been conducted with AMF inoculation under potted conditions, while field inoculation of AMF on citrus, especially a high economic hybrid tangor variety Beni-Madonna ( [...] Read more.
Citrus plants rely heavily on arbuscular mycorrhizal fungi (AMF) due to their lack of root hairs. Most experiments have been conducted with AMF inoculation under potted conditions, while field inoculation of AMF on citrus, especially a high economic hybrid tangor variety Beni-Madonna (Citrus nanko × C. amakusa), has been rarely recorded. This study aimed to analyze the effects of two AMF inoculations (a single Funneliformis mosseae and a mixture of F. mosseae, Diversispora versiformis, and Rhizophagus intraradices) on the internal and external fruit quality, leaf antioxidant defense system, and soil fertility and structure of top-worked Beni-Madonna tangor citrus trees. Three and a half years after AMF inoculations, soil hyphal length and root mycorrhizal colonization rate increased by 61.2–101.8% and 15.85–29.6% in inoculated plants, respectively. Inoculated trees had higher external fruit coloration value, fruit horizontal diameter, and fruit weight, and lower fruit rigidity than uninoculated trees. AMF-inoculated trees had higher glucose levels of fruit peels, fructose and sucrose levels of fruit fleshes, and the ratio of fruit soluble solids/titratable acids, as well as lower titratable acids concentrations than non-AMF-inoculated trees. AMF inoculation significantly increased leaf nitrogen balance index, chlorophyll index, peroxidase, catalase, superoxide dismutase, and glutathione reductase activities, as well as reduced glutathione and oxidized glutathione concentrations, resulting in lower hydrogen peroxide and malondialdehyde levels when compared to the uninoculated treatment. In addition, inoculated trees presented higher soil nutrient levels, including organic carbon, available K, and Olsen-P as, well as soil aggregate stability (based on mean weight diameter) than uninoculated trees. This study concluded that field AMF inoculation improved fruit quality, enhanced leaf antioxidant defense system, and improved soil fertility of Beni-Madonna trees, with mixed AMF being prominent in improving fruit quality and F. mosseae being prominent in enhancing leaf antioxidant defense system and improving soil fertility. Full article
(This article belongs to the Special Issue Microbes and Plant Stress Tolerance)
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15 pages, 8450 KiB  
Article
Machine Learning-Based Image Processing for Ice Concentration during Chukchi and Beaufort Sea Trials
by Huichan Kim, Sunho Park and Seong-Yeob Jeong
J. Mar. Sci. Eng. 2023, 11(12), 2281; https://doi.org/10.3390/jmse11122281 - 30 Nov 2023
Cited by 4 | Viewed by 1489
Abstract
Growing interest in finding the optimal route through the arctic ocean, and sea ice concentration is also emerging as a factor to be considered. In this paper, an algorithm to calculate the sea ice concentration was developed based on the images taken during [...] Read more.
Growing interest in finding the optimal route through the arctic ocean, and sea ice concentration is also emerging as a factor to be considered. In this paper, an algorithm to calculate the sea ice concentration was developed based on the images taken during the Arctic voyage of the Korean icebreaker ARAON in July 2019. A sea ice concentration calculation program was developed using the image processing functions in open-source image processing library, called OpenCV. To develop the algorithm, parameter studies were conducted on red, green, blue (RGB) color space and hue, saturation, value (HSV) color space, and k-means clustering. To verify the algorithm for sea ice concentration calculation, it was applied to images taken during Araon’s Arctic voyages. Lens curvature and view point were corrected through camera calibration. To improve the accuracy of sea ice concentration calculation, a binarization model based on random forest was proposed. A parameter study for training image numbers and tree numbers was conducted to establish the random forest model. The calculated sea ice concentrations by random forest and k-means clustering were compared and discussed. Full article
(This article belongs to the Section Ocean Engineering)
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15 pages, 2343 KiB  
Article
Provenance Identification of Leaves and Nuts of Bertholletia excelsa Bonpl by Near-Infrared Spectroscopy and Color Parameters for Sustainable Extraction
by Silvana Nisgoski, Joielan Xipaia dos Santos, Helena Cristina Vieira, Tawani Lorena Naide, Rafaela Stange, Washington Duarte Silva da Silva, Deivison Venicio Souza, Natally Celestino Gama and Márcia Orie de Souza Hamada
Sustainability 2023, 15(21), 15606; https://doi.org/10.3390/su152115606 - 3 Nov 2023
Viewed by 1734
Abstract
The Brazil nut tree is considered symbolic of the Brazilian Amazon in function of its great importance, being one of the most significant extractivist products and a subsistence practice of the Indigenous people in many municipalities in Pará state. One of the main [...] Read more.
The Brazil nut tree is considered symbolic of the Brazilian Amazon in function of its great importance, being one of the most significant extractivist products and a subsistence practice of the Indigenous people in many municipalities in Pará state. One of the main problems in different communities is related to the marketing process since it is not possible to distinguish the origin of the nuts and this causes inconvenience. The study evaluated the potential of VIS/NIR spectroscopy to identify the origin of leaves and nuts from Brazil nut trees growing in two indigenous villages, in the Xipaya Indigenous Lands, Pará state. Analysis was performed based on CIEL*a*b* parameters and using VIS (360–740 nm) and near-infrared spectra (1000–2500 nm). The samples were differentiated according to means tests, principal component analysis (PCA), and classification analysis based on k-NN. Color parameters and spectra were similar in both communities. Classification models based on k-NN produced adequate results for the distinction of villages in all evaluated situations, with accuracy of 98.54% for leaves, 89% and 90.91% for nuts with and without shell, respectively. Near infrared can be applied in forests as a technique for previous provenance identification and contribute to the subsistence and sustainable practice of extraction. Full article
(This article belongs to the Section Sustainable Forestry)
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25 pages, 35949 KiB  
Article
Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery
by Fahime Arabi Aliabad, Hamidreza Ghafarian Malamiri, Alireza Sarsangi, Aliihsan Sekertekin and Ebrahim Ghaderpour
Remote Sens. 2023, 15(16), 4053; https://doi.org/10.3390/rs15164053 - 16 Aug 2023
Cited by 16 | Viewed by 3601
Abstract
In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to [...] Read more.
In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings. Full article
(This article belongs to the Section Biogeosciences Remote Sensing)
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27 pages, 6394 KiB  
Article
Algebraic Multi-Layer Network: Key Concepts
by Igor Khanykov, Vadim Nenashev and Mikhail Kharinov
J. Imaging 2023, 9(7), 146; https://doi.org/10.3390/jimaging9070146 - 18 Jul 2023
Cited by 4 | Viewed by 1619
Abstract
The paper refers to interdisciplinary research in the areas of hierarchical cluster analysis of big data and ordering of primary data to detect objects in a color or in a grayscale image. To perform this on a limited domain of multidimensional data, an [...] Read more.
The paper refers to interdisciplinary research in the areas of hierarchical cluster analysis of big data and ordering of primary data to detect objects in a color or in a grayscale image. To perform this on a limited domain of multidimensional data, an NP-hard problem of calculation of close to optimal piecewise constant data approximations with the smallest possible standard deviations or total squared errors (approximation errors) is solved. The solution is achieved by revisiting, modernizing, and combining classical Ward’s clustering, split/merge, and K-means methods. The concepts of objects, images, and their elements (superpixels) are formalized as structures that are distinguishable from each other. The results of structuring and ordering the image data are presented to the user in two ways, as tabulated approximations of the image showing the available object hierarchies. For not only theoretical reasoning, but also for practical implementation, reversible calculations with pixel sets are performed easily, as with individual pixels in terms of Sleator–Tarjan Dynamic trees and cyclic graphs forming an Algebraic Multi-Layer Network (AMN). The detailing of the latter significantly distinguishes this paper from our prior works. The establishment of the invariance of detected objects with respect to changing the context of the image and its transformation into grayscale is also new. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
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