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Search Results (221)

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Keywords = vegetation color index

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19 pages, 1134 KiB  
Article
Application of Animal- and Plant-Derived Coagulant in Artisanal Italian Caciotta Cheesemaking: Comparison of Sensory, Biochemical, and Rheological Parameters
by Giovanna Lomolino, Stefania Zannoni, Mara Vegro and Alberto De Iseppi
Dairy 2025, 6(4), 43; https://doi.org/10.3390/dairy6040043 - 1 Aug 2025
Viewed by 75
Abstract
Consumer interest in vegetarian, ethical, and clean-label foods is reviving the use of plant-derived milk coagulants. Cardosins from Cynara cardunculus (“thistle”) are aspartic proteases with strong clotting activity, yet their technological impact in cheese remains under-explored. This study compared a commercial thistle extract [...] Read more.
Consumer interest in vegetarian, ethical, and clean-label foods is reviving the use of plant-derived milk coagulants. Cardosins from Cynara cardunculus (“thistle”) are aspartic proteases with strong clotting activity, yet their technological impact in cheese remains under-explored. This study compared a commercial thistle extract (PC) with traditional bovine rennet rich in chymosin (AC) during manufacture and 60-day ripening of Caciotta cheese. Classical compositional assays (ripening index, texture profile, color, solubility) were integrated with scanning electron microscopy, three-dimensional surface reconstruction, and descriptive sensory analysis. AC cheeses displayed slower but sustained proteolysis, yielding a higher and more linear ripening index, softer body, greater solubility, and brighter, more yellow appearance. Imaging revealed a continuous protein matrix with uniformly distributed, larger pores, consistent with a dairy-like sensory profile dominated by milky and umami notes. Conversely, PC cheeses underwent rapid early proteolysis that plateaued, producing firmer, chewier curds with lower solubility and darker color. Micrographs showed a fragmented matrix with smaller, heterogeneous pores; sensory evaluation highlighted vegetal, bitter, and astringent attributes. The data demonstrate that thistle coagulant can successfully replace animal rennet but generates cheeses with distinct structural and sensory fingerprints. The optimization of process parameters is therefore required when targeting specific product styles. Full article
(This article belongs to the Section Milk Processing)
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20 pages, 2735 KiB  
Article
Leaf Area Estimation in High-Wire Tomato Cultivation Using Plant Body Scanning
by Hiroki Naito, Tokihiro Fukatsu, Kota Shimomoto, Fumiki Hosoi and Tomohiko Ota
AgriEngineering 2025, 7(7), 206; https://doi.org/10.3390/agriengineering7070206 - 1 Jul 2025
Viewed by 484
Abstract
Accurate estimation of the leaf area index (LAI), a key indicator of canopy development and light interception, is essential for improving productivity in greenhouse tomato cultivation. This study presents a non-destructive LAI estimation method using side-view images captured by a vertical scanning system. [...] Read more.
Accurate estimation of the leaf area index (LAI), a key indicator of canopy development and light interception, is essential for improving productivity in greenhouse tomato cultivation. This study presents a non-destructive LAI estimation method using side-view images captured by a vertical scanning system. The system recorded the full vertical profile of tomato plants grown under two deleafing strategies: modifying leaf height (LH) and altering leaf density (LD). Vegetative and leaf areas were extracted using color-based masking and semantic segmentation with the Segment Anything Model (SAM), a general-purpose deep learning tool. Regression models based on leaf or all vegetative pixel counts showed strong correlations with destructively measured LAI, particularly under LH conditions (R2 > 0.85; mean absolute percentage error ≈ 16%). Under LD conditions, accuracy was slightly lower due to occlusion and leaf orientation. Compared with prior 3D-based methods, the proposed 2D approach achieved comparable accuracy while maintaining low cost and a labor-efficient design. However, the system has not been tested in real production, and its generalizability across cultivars, environments, and growth stages remains unverified. This proof-of-concept study highlights the potential of side-view imaging for LAI monitoring and calls for further validation and integration of leaf count estimation. Full article
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24 pages, 9205 KiB  
Article
Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images
by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang and Qingliang Cui
Agriculture 2025, 15(12), 1308; https://doi.org/10.3390/agriculture15121308 - 18 Jun 2025
Cited by 1 | Viewed by 473
Abstract
The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status [...] Read more.
The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R2 = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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15 pages, 2861 KiB  
Article
Agronomic and Quality Traits of 30 Eggplant Germplasm Resources from China
by Jian Lyu, Li Jin, Xianglan Ma, Yansu Li, Mintao Sun, Ning Jin, Shuya Wang, Linli Hu and Jihua Yu
Plants 2025, 14(12), 1838; https://doi.org/10.3390/plants14121838 - 15 Jun 2025
Viewed by 386
Abstract
(1) Background: Eggplant is a widely grown, high-value vegetable crop whose commercial demand has increased in recent years owing to its unique nutritional features. Variations in its agronomic and nutritional traits are of great importance in the selection of eggplant varieties. (2) Methods: [...] Read more.
(1) Background: Eggplant is a widely grown, high-value vegetable crop whose commercial demand has increased in recent years owing to its unique nutritional features. Variations in its agronomic and nutritional traits are of great importance in the selection of eggplant varieties. (2) Methods: In this study, 30 different eggplant varieties were evaluated concerning the morphological characteristics and nutritional value of their fruits. (3) Results: Among the eight morphological characteristics evaluated, the coefficient of variation was highest for fruit calyx thorns, pericarp brightness, and fruit shape index. The diversity index (H’) for pulp color was the largest, followed by pericarp brightness, but was the smallest for fruit weight. Principal component analysis showed that the morphological characteristics contributed 73.20% for the observed diversity among the 30 eggplant varieties, whereas eggplant fruit quality traits had a minor effect. Of note, significant differences in the soluble protein, vitamin C, nitrate, soluble sugar, organic acid, and mineral contents was observed within the samples, with organic acids, vitamin C, and hardness contributing more to the total variation observed. Multiple sets of correlations among the indices were found, with significant positive correlations between transverse diameter and hardness, fruit weight and fruit shape index, as well as between malic acid, fructose, and sucrose; (4) Conclusions: Altogether, these findings may help create breeding strategies to promote the selection of superior genotypes and help guide future germplasm collection. Full article
(This article belongs to the Section Crop Physiology and Crop Production)
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22 pages, 3331 KiB  
Article
Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision
by Wanna Fu, Zhen Chen, Qian Cheng, Yafeng Li, Weiguang Zhai, Fan Ding, Xiaohui Kuang, Deshan Chen and Fuyi Duan
Agriculture 2025, 15(12), 1272; https://doi.org/10.3390/agriculture15121272 - 12 Jun 2025
Viewed by 701
Abstract
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome [...] Read more.
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article
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21 pages, 5887 KiB  
Article
Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction
by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette and Bruno Tisseyre
Remote Sens. 2025, 17(11), 1846; https://doi.org/10.3390/rs17111846 - 25 May 2025
Viewed by 536
Abstract
Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study [...] Read more.
Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). This work utilizes the dataset produced by this experiment, incorporating other regressors and features derived from those collected in the field. Typically, crop yield prediction relies on features derived from direct field observations, either gathered through sensor measurements or manual data collection. However, enhancing prediction models by incorporating new features extracted through regressions executed on the original dataset features can potentially improve predictive outcomes. These extracted features, nominated in this work as meta-features (MFs), extracted through regressions with different regressors on original features, and incorporated into the dataset as new feature predictors, can be utilized in further regression analyses to optimize crop yield prediction. This study investigates the potential of generating MFs as an innovation to enhance sugarcane crop yield predictions. MFs were generated based on the values obtained by different regressors applied to the features collected in the field, allowing for evaluating which approaches offered superior predictive performance within the dataset. The kNN meta-regressor outperforms other regressors because it takes advantage of the proximity of MFs, which was checked through a projection where the dispersion of points can be measured. A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with R¯2 values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. The R¯2 values obtained in this work ranged above 0.98 for the AdaBoost meta-regressor applied to MFs, which were obtained from kNN regression on five models created by the researchers of CSIRO, and around 0.99 for the kNN meta-regressor applied to MFs obtained from kNN regression on these five models. Full article
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39 pages, 14246 KiB  
Article
Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
by Christian Massimiliano Baldin and Vittorio Marco Casella
Geosciences 2025, 15(5), 184; https://doi.org/10.3390/geosciences15050184 - 20 May 2025
Viewed by 1953
Abstract
Prior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible [...] Read more.
Prior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible through histogram matching to calibrate PlanetScope indices to achieve the same values as Sentinel-2 (useful also for proxy). Based on these findings, the authors examined the effectiveness of linear regression in aligning individual bands prior to computing indices to determine if the bands are shifted differently. The research was conducted on five important bands: Red, Green, Blue, NIR, and RedEdge. These bands allow for the computation of well-known vegetation indices like NDVI and NDRE, and soil indices like Iron Oxide Ratio and Coloration Index. Previous research showed that linear regression is not sufficient by itself to align indices in the worst-case scenario. However, this paper demonstrates its efficiency in achieving accurate band alignment. This finding highlights the importance of considering specific scaling requirements for bands obtained from different satellite sensors, such as PlanetScope and Sentinel-2. Contemporary images acquired by the two sensors during May and July demonstrated different behaviors in their bands; however, linear regression can align the datasets even during the problematic month of May. Full article
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22 pages, 4650 KiB  
Article
RGB Indices Can Be Used to Estimate NDVI, PRI, and Fv/Fm in Wheat and Pea Plants Under Soil Drought and Salinization
by Yuriy Zolin, Alyona Popova, Lyubov Yudina, Kseniya Grebneva, Karina Abasheva, Vladimir Sukhov and Ekaterina Sukhova
Plants 2025, 14(9), 1284; https://doi.org/10.3390/plants14091284 - 23 Apr 2025
Viewed by 692
Abstract
Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal [...] Read more.
Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors. Potentially, RGB indices can be used to estimate narrow-band reflectance indices and/or photosynthetic parameters in plants. Analysis of this problem was the main task of the current work. We investigated relationships of six RGB indices (r, g, b, ExG, VEG, and VARI) to widely used narrow-band reflectance indices (the normalized difference vegetation index, NDVI, and photochemical reflectance index, PRI) and the potential quantum yield of photosystem II (Fv/Fm) in wheat and pea plants under soil drought and salinization. It was shown that investigated RGB indices, NDVI, PRI, and Fv/Fm were significantly changed under the action of both stressors; changes in some RGB indices (e.g., ExG) were initiated on the early stage of action of drought or salinization. Correlation analysis showed that RGB indices (especially, ExG, VARY, and g) were strongly related to the NDVI, PRI, and Fv/Fm; linear regressions between these values were calculated. It means that RGB indices measured by simple and low-cost color cameras can be used to estimate plant parameters (NDVI, PRI, and Fv/Fm) requiring sophisticated equipment to measure. Full article
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28 pages, 5599 KiB  
Article
Multi-Source Feature Fusion Network for LAI Estimation from UAV Multispectral Imagery
by Lulu Zhang, Bo Zhang, Huanhuan Zhang, Wanting Yang, Xinkang Hu, Jianrong Cai, Chundu Wu and Xiaowen Wang
Agronomy 2025, 15(4), 988; https://doi.org/10.3390/agronomy15040988 - 20 Apr 2025
Cited by 1 | Viewed by 798
Abstract
The leaf area index (LAI) is a critical biophysical parameter that reflects crop growth conditions and the canopy photosynthetic potential, serving as a cornerstone in precision agriculture and dynamic crop monitoring. However, traditional LAI estimation methods relying on single-source remote sensing data and [...] Read more.
The leaf area index (LAI) is a critical biophysical parameter that reflects crop growth conditions and the canopy photosynthetic potential, serving as a cornerstone in precision agriculture and dynamic crop monitoring. However, traditional LAI estimation methods relying on single-source remote sensing data and often suffer from insufficient accuracy in high-density vegetation scenarios, limiting their capacity to reflect crop growth variability comprehensively. To overcome these limitations, this study introduces an innovative multi-source feature fusion framework utilizing unmanned aerial vehicle (UAV) multispectral imagery for precise LAI estimation in winter wheat. RGB and multispectral datasets were collected across seven different growth stages (from regreening to grain filling) in 2024. Through the extraction of color attributes, spatial structural information, and eight representative vegetation indices (VIs), a robust multi-source dataset was developed to integrate diverse data types. A convolutional neural network (CNN)-based feature extraction backbone, paired with a multi-source feature fusion network (MSF-FusionNet), was designed to effectively combine spectral and spatial information from both RGB and multispectral imagery. The experimental results revealed that the proposed method achieved superior estimation performance compared to single-source models, with an R2 of 0.8745 and RMSE of 0.5461, improving the R2 by 36.67% and 5.54% over the RGB and VI models, respectively. Notably, the fusion method enhanced the accuracy during critical growth phases, such as the regreening and jointing stages. Compared to traditional machine learning techniques, the proposed framework exceeded the performance of the XGBoost model, with the R2 rising by 4.51% and the RMSE dropping by 12.24%. Furthermore, our method facilitated the creation of LAI spatial distribution maps across key growth stages, accurately depicting the spatial heterogeneity and temporal dynamics in the field. These results highlight the efficacy and potential of integrating UAV multi-source data fusion with deep learning for precise LAI estimation in winter wheat, offering significant insights for crop growth evaluation and precision agricultural management. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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28 pages, 3329 KiB  
Article
PhenoCam Guidelines for Phenological Measurement and Analysis in an Agricultural Cropping Environment: A Case Study of Soybean
by S. Sunoj, C. Igathinathane, Nicanor  Saliendra, John Hendrickson, David Archer and Mark Liebig
Remote Sens. 2025, 17(4), 724; https://doi.org/10.3390/rs17040724 - 19 Feb 2025
Viewed by 981
Abstract
A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 [...] Read more.
A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 h–14:00 h) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing II)
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15 pages, 1969 KiB  
Article
The Effect of Optimizing the Stripping and Drying Parameters During Industrial Extraction on the Physicochemical Properties of Soybean Oil
by Toktam Mohammadi-Moghaddam, Hamid Bakhshabadi, Abolfazl Bojmehrani, Marcos Eduardo Valdes and Afsaneh Morshedi
Processes 2025, 13(2), 541; https://doi.org/10.3390/pr13020541 - 14 Feb 2025
Cited by 1 | Viewed by 930
Abstract
Soybean oil is the second most consumed vegetable oil worldwide and is recognized as a source of heart-healthy polyunsaturated fatty acids. Optimizing the extraction process in the oil industry is essential for both economic and environmental sustainability. This research aimed to determine the [...] Read more.
Soybean oil is the second most consumed vegetable oil worldwide and is recognized as a source of heart-healthy polyunsaturated fatty acids. Optimizing the extraction process in the oil industry is essential for both economic and environmental sustainability. This research aimed to determine the optimal conditions for various extraction parameters—stripper temperature (110–140 °C), stripper pressure (150–210 mbar), and dryer pressure (60–120 mbar)—and their effects on the physicochemical properties of soybean oil. These properties include oil-insoluble fine substances, acidity, the color index, peroxide value, oxidative stability, and moisture content. The results indicated that the stripper temperature significantly influenced oil-insoluble fine substances, acidity, the color index, and peroxide value (p < 0.05). The optimal conditions for oil extraction were found to be a stripper temperature of 110 °C, a stripper pressure of 150 mbar, and a dryer pressure of 120 mbar. Under these conditions, the oil-insoluble fine substances, acidity, the color index, peroxide value, oxidative stability, and moisture content of soybean oil were in the ranges of 0.2–0.58%, 0.63–1.15%, 4.3–5.5, 0.67–1.23 meqO2/kg, 3–5.5, and 0.05–0.11%, respectively. These findings provide valuable insight for optimizing soybean oil extraction processes to enhance quality and efficiency. Future advancements in industrial oil extraction are expected to focus on integrating efficient, eco-friendly technologies and enhancing precision through automation and data analytics to optimize yield and minimize waste. Full article
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22 pages, 6176 KiB  
Article
The Distribution of Microplastic Pollution and Ecological Risk Assessment of Jingpo Lake—The World’s Second Largest High-Mountain Barrier Lake
by Haitao Wang, Chen Zhao and Tangbin Huo
Biology 2025, 14(2), 201; https://doi.org/10.3390/biology14020201 - 14 Feb 2025
Viewed by 1402
Abstract
To investigate the influence of factors such as tourism, agriculture, and population density on the presence of microplastic (MP) content in aquatic environments and their associated ecological risks, Jingpo Lake, a remote high-mountain lake situated away from urban areas, was selected as the [...] Read more.
To investigate the influence of factors such as tourism, agriculture, and population density on the presence of microplastic (MP) content in aquatic environments and their associated ecological risks, Jingpo Lake, a remote high-mountain lake situated away from urban areas, was selected as the research subject. This study examined the abundance, types, sizes, colors, and polymer compositions of MPs within the water body, fish, and sediments. By considering variables, including fishing practices, agricultural activities, population dynamics, and vegetation cover, an analysis was conducted to unravel the spatial and temporal distribution of MPs concerning human activities, ultimately leading to an assessment of the ecological risks posed by MP pollution. The findings revealed that the average abundance of MPs in the lake’s surface water was recorded as (304.8 ± 170.5) n/m3, while in the sediments, it averaged (162.0 ± 57.45) n/kg. Inside the digestive tracts of fish, the MP abundance was measured at 11.4 ± 5.4 n/ind. The contamination of MPs within the aquatic environment of Jingpo Lake was found to be relatively minimal. Variations in MP loads across time and space were observed, with MPs predominantly falling within the size range of small planktonic organisms (50–1000 μm). Additionally, the prevalent colors of MPs in the water samples were white or transparent, constituting approximately 55.65% of the entire MP composition. Subsequently, they were black, red, and blue. This colors distribution were consistent across MPs extracted from fish and sediment samples. The chemical compositions of the MPs predominantly comprised PE (31.83%) and PS (25.48%), followed by PP (17.56%), PA (11.84%), PET (6.71%), EVA (4.56%), and PC (2.03%). Regarding the seasonal aspect, MP concentrations were highest during summer (46.68%), followed by spring (36.75%) and autumn (16.56%). The spatial distribution of MPs within Jingpo Lake’s water body, fish, and sediments was notably influenced by human activities, as confirmed by Pearson correlation coefficients. A strong association was observed between MP levels and water quality indicators such as ammonium nitrogen (NH4-N), total phosphorus (TP), and chlorophyll-a (Chla), suggesting that human-related pollution contributed significantly to MP contamination. The diversity assessment of MP pollutants exhibited the highest variability in chemical composition (1.23 to 1.79) using the Shannon–Wiener Index. Subsequently, the diversity of colors ranged from 0.59 to 1.54, shape diversity from 0.78 to 1.30, seasonal diversity from 0.83 to 1.10, and size diversity from 0.44 to 1.01. The assessment results of ecological risk highlighted that the risk categories for MPs within the surface water, fish, and sediments of Jingpo Lake were categorized as I for the PHI and PLI and as “Minor” for the PERI. These relatively low-risk values were attributed to the predominantly low toxicity of the distributed MPs within the Jingpo Lake basin. Moreover, the results of the risk assessment were found to be interconnected with the distribution of the local population and agricultural activities around the sampling sections. Usage patterns of coastal land and population density were recognized as influential factors affecting MP loads within the water body, sediments, fish, and other components of the lake ecosystem. Full article
(This article belongs to the Special Issue Global Fisheries Resources, Fisheries, and Carbon-Sink Fisheries)
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17 pages, 3052 KiB  
Article
Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data Based on Machine-Learning Algorithm
by Minhuan Hu, Jingshu Wang, Peng Yang, Ping Li, Peng He and Rutian Bi
Agronomy 2025, 15(2), 456; https://doi.org/10.3390/agronomy15020456 - 13 Feb 2025
Cited by 1 | Viewed by 945
Abstract
Rapid and accurate leaf area index (LAI) determination is important for monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as [...] Read more.
Rapid and accurate leaf area index (LAI) determination is important for monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI), and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and the color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. The selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others ( r = 0.711, RMSE = 0.498, and NRMSE = 9.10%). This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management. Full article
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13 pages, 2019 KiB  
Technical Note
LeafLaminaMap: Exploring Leaf Color Patterns Using RGB Color Indices
by Péter Bodor-Pesti, Lien Le Phuong Nguyen, Thanh Ba Nguyen, Mai Sao Dam, Dóra Taranyi and László Baranyai
AgriEngineering 2025, 7(2), 39; https://doi.org/10.3390/agriengineering7020039 - 6 Feb 2025
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Abstract
The color of the plant leaves is a major concern in many areas of agriculture. Pigmentation and its pattern provide the possibility to distinguish genotypes and a basis for annual crop management practices. For example, the nutrient and water status of plants is [...] Read more.
The color of the plant leaves is a major concern in many areas of agriculture. Pigmentation and its pattern provide the possibility to distinguish genotypes and a basis for annual crop management practices. For example, the nutrient and water status of plants is reflected in the chlorophyll content of leaves that are strongly linked to the lamina coloration. Pests and diseases (virus or bacterial infections) also cause symptoms on the foliage. These symptoms induced by biotic and abiotic stressors often have a specific pattern, which allows for their prediction based on remote sensing. In this report, an RGB (red, green and blue) image processing system is presented to determine leaf lamina color variability based on RGB-based color indices. LeafLaminaMap was developed in Scilab with the Image Processing and Computer Vision toolbox, and the code is available freely at GitHub. The software uses RGB images to visualize 29 color indices and the R, G and B values on the lamina, as well as to calculate the statistical parameters. In this case study, symptomatic (senescence, fungal infection, etc.) and healthy grapevine (Vitis vinifera L.) leaves were collected, digitalized and analyzed with the LeafLaminaMap software according to the mean, standard deviation, contrast, energy and entropy of each channel (R, G and B) and color index. As an output for each original image in the sample set, the program generates 32 images, where each pixel is constructed using index values calculated from the RGB values of the corresponding pixel in the original image. These generated images can subsequently be used to help the end-user identify locally occurring symptoms that may not be visible in the original RGB image. The statistical evaluation of the samples showed significant differences in the color pattern between the healthy and symptomatic samples. According to the F value of the ANOVA analysis, energy and entropy had the largest difference between the healthy and symptomatic samples. Linear discriminant analysis (LDA) and support vector machine (SVM) analysis provided a perfect recognition in calibration and confirmed that energy and entropy have the strongest discriminative power between the healthy and symptomatic samples. The case study showed that the LeafLaminaMap software is an effective environment for the leaf lamina color pattern analysis; moreover, the results underline that energy and entropy are valuable features and could be more effective than the mean and standard deviation of the color properties. Full article
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27 pages, 24351 KiB  
Article
UAV-Based Multiple Sensors for Enhanced Data Fusion and Nitrogen Monitoring in Winter Wheat Across Growth Seasons
by Jingjing Wang, Wentao Wang, Suyi Liu, Xin Hui, Haohui Zhang, Haijun Yan and Wouter H. Maes
Remote Sens. 2025, 17(3), 498; https://doi.org/10.3390/rs17030498 - 31 Jan 2025
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Abstract
Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study’s objective was to analyze UAV multiple sensors’ performance in monitoring winter [...] Read more.
Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study’s objective was to analyze UAV multiple sensors’ performance in monitoring winter wheat chlorophyll content (SPAD), plant nitrogen accumulation (PNA), and N nutrition index (NNI). A two-year field experiment with five N fertilizer treatments was carried out. The color indices (CIs, from RGB sensors), vegetation indices (VIs, from multispectral sensors), and temperature indices (TIs, from thermal sensors) were derived from the collected images. XGBoost (extreme gradient boosting) was applied to develop the models, using 2021 data for training and 2022 data for testing. The excess green minus excess red index, red green ratio index, and hue (from CIs), and green normalized difference vegetation index, normalized difference red-edge index, and normalized difference vegetation index (from VIs), showed high correlations with three N indicators. At the pre-heading stage, the best performing CIs correlated better than the VIs; this was reversed in the post-heading stage. CIs outperformed VIs in SPAD (CIs: R2(coefficient of determination) = 0.66, VIs: R2 = 0.61), PNA (CIs: R2 = 0.68, VIs: R2 = 0.64), and NNI (CIs: R2 = 0.64, VIs: R2 = 0.60) in the pre-heading stage, whereas VI-based models achieved slightly higher accuracies in post-heading and all stages compared to CIs. Models built with CIs + VIs significantly improved the models’ performance compared to single-sensor models. Adding TIs to CIs and CIs + VIs further improved the models’ performance slightly, especially at the post-heading stage, resulting in the best model performance with three sensors. These findings highlight the effectiveness of UAV systems in estimating wheat N and establish a framework for integrating RGB, multispectral, and thermal sensors to enhance model accuracy in precision vegetation monitoring. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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