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Keywords = canopy cover (CC)

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20 pages, 10592 KiB  
Article
Use of Uncrewed Aerial System (UAS)-Based Crop Features to Perform Growth Analysis of Energy Cane Genotypes
by Ittipon Khuimphukhieo, Lei Zhao, Benjamin Ghansah, Jose L. Landivar Scott, Oscar Fernandez-Montero, Jorge A. da Silva, Jamie L. Foster, Hua Li and Mahendra Bhandari
Plants 2025, 14(5), 654; https://doi.org/10.3390/plants14050654 - 21 Feb 2025
Viewed by 351
Abstract
Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype [...] Read more.
Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype data for growth analysis. The objectives of this study were to obtain high-temporal UAS-based phenotype data for growth analysis and investigate the correlation between the UAS-based phenotype and biomass yield. Seven different energy cane genotypes were grown in a random complete block design with four replications. Twenty-six UAS flight missions were flown throughout the growing season, and canopy cover (CC) and canopy height (CH) measurements were extracted. A five-parameter logistic (5PL) function was fitted through these temporal measurements of CC and CH. The first- and second-order derivatives of this function were calculated to obtain several growth parameters, which were then used to assess the growth of different genotypes with respect to weed competitiveness and biomass yield traits. The results show that CC and CH growth rates significantly differed among genotypes. TH16-16 was outstanding for its ground cover growth; therefore, it was identified as a weed-competitive genotype. Furthermore, TH16-22 had a higher CH maximum growth rate per day, yielding a higher biomass compared to other genotypes. The CH-based multi-temporal data as well as the growth parameters had a better relationship with biomass yield. This study highlights the application of UAS-based high-throughput phenotyping (HTP), along with growth analysis, for assisting plant breeders in decision-making. Full article
(This article belongs to the Special Issue Modeling of Plants Phenotyping and Biomass)
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25 pages, 4935 KiB  
Article
From Air to Space: A Comprehensive Approach to Optimizing Aboveground Biomass Estimation on UAV-Based Datasets
by Muhammad Nouman Khan, Yumin Tan, Lingfeng He, Wenquan Dong and Shengxian Dong
Forests 2025, 16(2), 214; https://doi.org/10.3390/f16020214 - 23 Jan 2025
Viewed by 940
Abstract
Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of [...] Read more.
Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of GEDI-L4A AGB and GEDI-L2A rh98 heights, and spectral variables derived from UAV-multispectral and RGB data were assessed. These calibrated AGB and height values and UAV-derived spectral variables were used to fit AGB estimations using a random forest (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 of the most important predictor variables in the AGB prediction model, including calibrated GEDI AGB and height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio Index (RBRI), Difference Vegetation Index (DVI), canopy cover (CC), Atmospherically Resistant Vegetation Index (ARVI), Red-Edge Normalized Difference Vegetation Index (NDVIre), Color Index of Vegetation (CIVI), elevation, and slope. The results showed that, in general, the second model based on calibrated AGB and height, Sentinel-2 indices, slope and elevation, and spectral variables from UAV-multispectral and RGB datasets with evaluation metric (for training: R2 = 0.941 Mg/ha, RMSE = 13.514 Mg/ha, MAE = 8.136 Mg/ha) performed better than the first model with AGB prediction. The result was between 23.45 Mg/ha and 301.81 Mg/ha, and the standard error was between 0.14 Mg/ha and 10.18 Mg/ha. This hybrid approach significantly improves AGB prediction accuracy and addresses uncertainties in AGB prediction modeling. The findings provide a robust framework for enhancing forest carbon stock assessment and contribute to global-scale AGB monitoring, advancing methodologies for sustainable forest management and ecological research. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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26 pages, 20467 KiB  
Article
Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field
by Hanyu Ma, Weiliang Wen, Wenbo Gou, Yuqiang Liang, Minggang Zhang, Jiangchuan Fan, Shenghao Gu, Dongsheng Zhang and Xinyu Guo
Agriculture 2025, 15(1), 6; https://doi.org/10.3390/agriculture15010006 - 24 Dec 2024
Viewed by 526
Abstract
The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a [...] Read more.
The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a rail-driven high-throughput plant phenotyping platform (HTPPP) in field conditions. An adaptive sliding window segmentation algorithm was proposed to obtain plots and rows from canopy point clouds. Maximum height (Hmax), mean height (Hmean), and canopy cover (CC) of each plot were extracted, and quantification of plot canopy height uniformity (CHU) and marginal effect (MEH) was achieved. The results showed that the average mIoU, mP, mR, and mF1 of canopy–plot segmentation were 0.8118, 0.9587, 0.9969, and 0.9771, respectively, and the average mIoU, mP, mR, and mF1 of plot–row segmentation were 0.7566, 0.8764, 0.9292, and 0.8974, respectively. The average RMSE of plant height across the 10 growth stages was 0.08 m. The extracted time-series phenotypes show that CHU tended to vary from uniformity to nonuniformity and continued to fluctuate during the whole growth stages, and the MEH of the canopy tended to increase negatively over time. This study provides automated and practical means for 3D time-series phenotype monitoring of plant canopies with the HTPPP. Full article
(This article belongs to the Section Digital Agriculture)
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17 pages, 13998 KiB  
Article
Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning
by Ittipon Khuimphukhieo, Jose Carlos Chavez, Chuanyu Yang, Lakshmi Akhijith Pasupuleti, Ismail Olaniyi, Veronica Ancona, Kranthi K. Mandadi, Jinha Jung and Juan Enciso
Sensors 2024, 24(23), 7646; https://doi.org/10.3390/s24237646 - 29 Nov 2024
Viewed by 952
Abstract
Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that [...] Read more.
Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2024)
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21 pages, 3169 KiB  
Article
Using Legume-Enriched Cover Crops to Improve Grape Yield and Quality in Hillside Vineyards
by Oriana Silvestroni, Edoardo Dottori, Luca Pallotti, Tania Lattanzi, Rodolfo Santilocchi and Vania Lanari
Agronomy 2024, 14(11), 2528; https://doi.org/10.3390/agronomy14112528 - 28 Oct 2024
Cited by 1 | Viewed by 1327
Abstract
Natural covering (NATC) has spread on hillside vineyards of central Italy as a replacement for tillage to reduce soil erosion, although it increased nitrogen and water needs. Therefore, in the current context of global warming, using cover crops (CCs) that require less water [...] Read more.
Natural covering (NATC) has spread on hillside vineyards of central Italy as a replacement for tillage to reduce soil erosion, although it increased nitrogen and water needs. Therefore, in the current context of global warming, using cover crops (CCs) that require less water and provide nitrogen becomes crucial. The effects of two low-competition legume-enriched CCs in a rainfed hillside vineyard—a perennial legume–grass mixture (PLGM) and an annual legume cover crop of Trifolium alexandrinum (ALTA)—were compared with NATC over three years. PLGM and ALTA provided good levels of soil coverage, slightly lower than NATC, which had a negligible presence of legumes. PLGM and ALTA, due to low competition, enhanced vine vigor, resulting in thicker and wider canopies (as indicated by total leaf area and leaf layer number), higher pruning weight, and increased yield. PLGM and ALTA led to good qualitative levels, with higher grapes acidities, lower pH and total soluble solids content and, additionally, significantly higher yeast assimilable nitrogen content. In conclusion, implementing low-competition legume species in CCs is an effective tool to avoid soil erosion in a climate change scenario, leading to increased productivity, higher acidity, and improved nitrogen content in the grapes. Full article
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18 pages, 7108 KiB  
Article
Inversion of Soybean Net Photosynthetic Rate Based on UAV Multi-Source Remote Sensing and Machine Learning
by Zhen Lu, Wenbo Yao, Shuangkang Pei, Yuwei Lu, Heng Liang, Dong Xu, Haiyan Li, Lejun Yu, Yonggang Zhou and Qian Liu
Agronomy 2024, 14(7), 1493; https://doi.org/10.3390/agronomy14071493 - 10 Jul 2024
Viewed by 969
Abstract
Net photosynthetic rate (Pn) is a common indicator used to measure the efficiency of photosynthesis and growth conditions of plants. In this study, soybeans under different moisture gradients were selected as the research objects. Fourteen vegetation indices (VIS) and five canopy structure characteristics [...] Read more.
Net photosynthetic rate (Pn) is a common indicator used to measure the efficiency of photosynthesis and growth conditions of plants. In this study, soybeans under different moisture gradients were selected as the research objects. Fourteen vegetation indices (VIS) and five canopy structure characteristics (CSC) (plant height (PH), volume (V), canopy cover (CC), canopy length (L), and canopy width (W)) were obtained using an unmanned aerial vehicle (UAV) equipped with three different sensors (visible, multispectral, and LiDAR) at five growth stages of soybeans. Soybean Pn was simultaneously measured manually in the field. The variability of soybean Pn under different conditions and the trend change of CSC under different moisture gradients were analysed. VIS, CSC, and their combinations were used as input features, and four machine learning algorithms (multiple linear regression, random forest, Extreme gradient-boosting tree regression, and ridge regression) were used to perform soybean Pn inversion. The results showed that, compared with the inversion model using VIS or CSC as features alone, the inversion model using the combination of VIS and CSC features showed a significant improvement in the inversion accuracy at all five stages. The highest accuracy (R2 = 0.86, RMSE = 1.73 µmol m−2 s−1, RPD = 2.63) was achieved 63 days after sowing (DAS63). Full article
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14 pages, 4229 KiB  
Article
Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height
by Uriel Cholula, Manuel A. Andrade and Juan K. Q. Solomon
AgriEngineering 2024, 6(3), 2101-2114; https://doi.org/10.3390/agriengineering6030123 - 8 Jul 2024
Viewed by 912
Abstract
In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area [...] Read more.
In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models. Full article
(This article belongs to the Section Agricultural Irrigation Systems)
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17 pages, 5448 KiB  
Article
Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops
by Sambandh Bhusan Dhal, Stavros Kalafatis, Ulisses Braga-Neto, Krishna Chaitanya Gadepally, Jose Luis Landivar-Scott, Lei Zhao, Kevin Nowka, Juan Landivar, Pankaj Pal and Mahendra Bhandari
Remote Sens. 2024, 16(11), 1906; https://doi.org/10.3390/rs16111906 - 25 May 2024
Cited by 6 | Viewed by 1968
Abstract
Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This [...] Read more.
Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production. Full article
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16 pages, 6714 KiB  
Article
Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains
by Avay Risal, Haoyu Niu, Jose Luis Landivar-Scott, Murilo M. Maeda, Craig W. Bednarz, Juan Landivar-Bowles, Nick Duffield, Paxton Payton, Pankaj Pal, Robert J. Lascano, Timothy Goebel and Mahendra Bhandari
Water 2024, 16(9), 1300; https://doi.org/10.3390/w16091300 - 2 May 2024
Cited by 5 | Viewed by 1843
Abstract
The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area [...] Read more.
The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP. Full article
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18 pages, 9046 KiB  
Article
Application of UAV Multispectral Imaging to Monitor Soybean Growth with Yield Prediction through Machine Learning
by Sadia Alam Shammi, Yanbo Huang, Gary Feng, Haile Tewolde, Xin Zhang, Johnie Jenkins and Mark Shankle
Agronomy 2024, 14(4), 672; https://doi.org/10.3390/agronomy14040672 - 26 Mar 2024
Cited by 9 | Viewed by 2421
Abstract
The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral images in five different spectral bands (blue, [...] Read more.
The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral images in five different spectral bands (blue, green, red, red edge, and near-infrared), instead of satellite-captured data, to monitor soybean growth in a field. The field experiment was conducted in a soybean field at the Mississippi State University Experiment Station near Pontotoc, MS, USA. The experiment consisted of five cover crops (Cereal Rye, Vetch, Wheat, Mustard plus Cereal Rye, and native vegetation) planted in the winter and three fertilizer treatments (Fertilizer, Poultry Liter, and None) applied before planting the soybean. During the soybean growing season in 2022, eight UAV imaging flyovers were conducted, spread across the growth season. UAV image-derived vegetation indices (VIs) coupled with machine learning (ML) models were computed for characterizing soybean growth at different stages across the season. The aim of this study focuses on monitoring soybean growth to predict yield, using 14 VIs including CC (Canopy Cover), NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2), and others. Different machine learning algorithms including Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are used for this purpose. The stage of the initial pod development was shown as having the best predictability for earliest soybean yield prediction. CC, NDVI, and NAVI (Normalized area vegetation index) were shown as the best VIs for yield prediction. The RMSE was found to be about 134.5 to 511.11 kg ha−1 in the different yield models, whereas it was 605.26 to 685.96 kg ha−1 in the cross-validated models. Due to the limited number of training and testing samples in the K-fold cross-validation, the models’ results changed to some extent. Nevertheless, the results of this study will be useful for the application of UAV remote sensing to provide information for soybean production and management. This study demonstrates that VIs coupled with ML models can be used in multistage soybean yield prediction at a farm scale, even with a limited number of training samples. Full article
(This article belongs to the Special Issue Crop Production Parameter Estimation through Remote Sensing Data)
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23 pages, 6140 KiB  
Article
UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests
by Lingjun Zhang, Yinyin Zhao, Chao Chen, Xuejian Li, Fangjie Mao, Lujin Lv, Jiacong Yu, Meixuan Song, Lei Huang, Jinjin Chen, Zhaodong Zheng and Huaqiang Du
Remote Sens. 2024, 16(4), 705; https://doi.org/10.3390/rs16040705 - 17 Feb 2024
Cited by 8 | Viewed by 2136
Abstract
Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within [...] Read more.
Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R2 values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R2 = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R2 reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources. Full article
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13 pages, 1226 KiB  
Article
Impact of Cover Cropping on Temporal Nutrient Distribution and Availability in the Soil
by Miurel Brewer, Ramdas G. Kanissery, Sarah L. Strauss and Davie M. Kadyampakeni
Horticulturae 2023, 9(10), 1160; https://doi.org/10.3390/horticulturae9101160 - 22 Oct 2023
Cited by 7 | Viewed by 2035
Abstract
Cover cropping is a best management practice that can improve soil quality by reducing soil erosion, building soil organic matter (SOM), and improving soil nutrient availability. Southwest (SW) Florida citrus growers have the challenge of growing citrus in sandy soils characterized by low [...] Read more.
Cover cropping is a best management practice that can improve soil quality by reducing soil erosion, building soil organic matter (SOM), and improving soil nutrient availability. Southwest (SW) Florida citrus growers have the challenge of growing citrus in sandy soils characterized by low organic matter (<2%), extremely low water and nutrient-holding capacities, and high sand content (>90%), and therefore are looking for methods to improve SOM and nutrient retention and availability in sandy soils. A trial of two cover crop (CC) mixtures planted in the row middles (RM) of Huanglongbing-affected citrus ‘Valencia’ (Citrus sinensis (L.) Osbeck) orchards in sandy soils in SW Florida was conducted. This study explored how incorporating CCs in the RM of the orchards could affect soil ammonium (NH4+), soil nitrate (NO3), exchangeable macronutrients, and SOM temporal availability. These parameters were measured under the tree canopy (UC) and within RM of two orchards: South Grove (SG) and North Grove (NG), both located in SW Florida. The two seeded CC mixtures were legume + non-legume (LG+NL) and non-legume (NL) and were compared to a control no-CC grower standard (GSC). Phosphorus, calcium, magnesium, and NH4+ were not statistically significantly different among treatments in either of the two sampling positions (UC and RM). Cover cropping significantly (p < 0.05) increased NO3-N concentrations in the RM area of the citrus orchards after seven consecutive seasons (brassicas, legumes, and grasses) by 31% in the LG + NL and 29% in the NL with reference to the GSC. In addition to the significant increase in NO3N, SOM significantly (p < 0.05) increased in the RM in the NG site only in both CCs treatments by 17% and 16% for LG + NL and NL treatments, respectively, compared with GSC. Full article
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21 pages, 4908 KiB  
Article
Rapid and Non-Destructive Methodology for Measuring Canopy Coverage at an Early Stage and Its Correlation with Physiological and Morphological Traits and Yield in Sugarcane
by Raja Arun Kumar, Srinivasavedantham Vasantha, Raju Gomathi, Govindakurup Hemaprabha, Srinivasan Alarmelu, Venkatarayappa Srinivasa, Krishnapriya Vengavasi, Muthalagu Alagupalamuthirsolai, Kuppusamy Hari, Chinappagounder Palaniswami, Krishnasamy Mohanraj, Chinnaswamy Appunu, Ponnaiyan Geetha, Arjun Shaligram Tayade, Shareef Anusha, Vazhakkannadi Vinu, Ramanathan Valarmathi, Pooja Dhansu and Mintu Ram Meena
Agriculture 2023, 13(8), 1481; https://doi.org/10.3390/agriculture13081481 - 26 Jul 2023
Cited by 4 | Viewed by 2381
Abstract
Screening for elite sugarcane genotypes for canopy cover in a rapid and non-destructive way is important to accelerate varietal/clonal selection, and little information is available regarding canopy cover and leaf production, leaf area, biomass production, and cane yield in sugarcane crop. In the [...] Read more.
Screening for elite sugarcane genotypes for canopy cover in a rapid and non-destructive way is important to accelerate varietal/clonal selection, and little information is available regarding canopy cover and leaf production, leaf area, biomass production, and cane yield in sugarcane crop. In the present investigation, the digital images of sugarcane crop by using Canopeo software was assessed for their correlation with the physiological and morphological parameters and cane yield production. The results revealed that among the studied parameters, canopy coverage has shown a significantly better correlation with the plant height (0.581 **), leaf length (0.853 **), leaf width (0.587 **), and leaf area (0.770 **) in commercial sugarcane clones. Two-way cluster analysis has led to the identification of Co 0238, Co 86249, Co 10026, Co 99004, Co 94008, and Co 95020 with better physiological traits for higher sugarcane yield under changing climate. Additionally, in another field experiment with pre-breeding, germplasm, and interspecific hybrid sugarcane clones, the canopy coverage showed a significantly better correlation with germination, shoot count, leaf weight, leaf area index, and plant height, and finally with biomass (r = 0.612 **) and cane yield (r = 0.458 **). It has been found that the plant height, total dry matter (TDM), and leaf area index (LAI) had significant correlation with the cane yield, and the canopy cover data from digital images act as a surrogate for these traits, and further it has been observed that CC had better correlation with cane yield compared to the other physiological traits viz., SPAD, total chlorophyll (TC), and canopy temperature (CT) under ambient conditions. Light interception determined using a line quantum sensor had a significant positive correlation (r = 0.764 **) with canopy coverage, signifying the importance of determining the latter in a non-destructive way in a rapid manner and low cost. Full article
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16 pages, 4271 KiB  
Article
Optimizing Rice Irrigation Strategies to Maximize Water Productivity: A Simulation Study Using AquaCrop Model for the Yanyun Irrigation District, Yangzhou, China
by Monera Mostafa, Wan Luo, Jiarong Zou and Ali Salem
Earth 2023, 4(3), 445-460; https://doi.org/10.3390/earth4030024 - 28 Jun 2023
Cited by 3 | Viewed by 2024
Abstract
The AquaCrop model is used to predict rice yield in response to different irrigation management in the Yanyun irrigation area in Yangzhou, China, and the constraints to rice production were identified to maximize water productivity based on model simulations. The model was calibrated [...] Read more.
The AquaCrop model is used to predict rice yield in response to different irrigation management in the Yanyun irrigation area in Yangzhou, China, and the constraints to rice production were identified to maximize water productivity based on model simulations. The model was calibrated by comparing measured and predicted canopy cover (CC), yield, and soil water content during the growing season in 2018. The results showed that, for CC simulations, R2 was 0.99, RMSE was 3.6%, and NRMSE was 5.3%; for Biomass simulation, RMSE was 0.50 t/ha, and NRMSE was 5.3%. Different irrigation strategies were analyzed for a long-term simulation period from 1955 to 2014. The simulated rice yield increased rapidly as irrigation demand increased initially, and then gradually stabilized. The simulated rice yield fluctuated in the different years. The Pearson type-III model method was used to identify different hydrological years of wet, normal, and dry years. The analysis identified the wet year as 1991, normal year as 1981, and dry year as 1966. In the different rainfall years (1991, 1981, and 1966) water use efficiency (WUE), water productivity (WPet), and irrigation water productivity (IWP) were utilized to determine the irrigation strategy. The predicted highest WPet in the wet year was 1.77kg m−3, while the lowest WPet in the dry year was 1.13 kg m−3. The highest IWP was 19.78 kg m−3 in the wet year, and 9.32 kg m−3 in the normal year; while the lowest IWP in the dry year was 1.90 kg m−3. IWP was significantly higher in the rainy year, while WUE was significantly lower. On the other hand, WPet was more extensive in the wet year because the yield was higher, and the Evapotranspiration (ET) was smaller in comparison to the dry year. Full article
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18 pages, 3722 KiB  
Article
Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass
by Robin Kümmerer, Patrick Ole Noack and Bernhard Bauer
Remote Sens. 2023, 15(6), 1520; https://doi.org/10.3390/rs15061520 - 10 Mar 2023
Cited by 12 | Viewed by 4791
Abstract
Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the [...] Read more.
Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs). Images captured with red–green–blue cameras mounted on UAVs in two missions varying in ground sample distances were used as input for generating three-dimensional point clouds using the structure-from-motion approach. These point clouds were then compared to manual ground measurements. The results showed that the agreement between the methods was closest when CC presented dense and smooth canopies. However, stands with rough canopies or gaps showed substantial differences between the UAV method and ground measurements. We conclude that the UAV method is substantially more precise and accurate in determining CH than measurements taken with a ruler since the UAV introduces additional dimensions with greatly increased resolution. CH can be a reliable indicator of biomass yield, but no differences between the investigated methods were found, probably due to allometric variations of different CC species. We propose the presented UAV method as a promising tool to include site-specific information on CC in crop production strategies. Full article
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