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学者姓名：张厚喜
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Accurate estimation of fractional cover of Chinese fir (Cunninghamialanceolata (Lamb.) Hook.) (FCCF) is crucial for forest management and carbon sequestration assessment. Traditional field inventory is time-consuming, labor-intensive, prone to sampling bias, and often mismatched with satellite image resolutions, hindering accurate regional FCCF mapping. In this study, we propose an upscaling framework for regional estimation of FCCF integrating unmanned aerial vehicle (UAV) and satellite imagery. High-precision classification labels were generated from UAV visible imagery using three methods: object-based image analysis with random forest (OBIA-RF), maximum likelihood, and minimum distance classifiers. The resulting binary classification maps were then aggregated to different spatial resolutions and linked with Sentinel-2A (10 m and 20 m) and Landsat 8 OLI (30 m) data to develop FCCF estimation models. We evaluated the performance of four models, including linear regression (LR), RF, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). SHapley Additive exPlanations (SHAP) values were employed to quantify the influence of vegetation indices (VIs) derived from satellite images on model accuracy. Results showed that OBIA-RF achieved the highest accuracy for Chinese fir classification based on UAV images, with an average overall accuracy and recall of 0.919 and 0.909, respectively. At the satellite scale, the LightGBM, RF, and XGBoost models had the best FCCF estimates at 10 m, 20 m, and 30 m spatial resolutions with R$<^>2$2 of 0.569, 0.762, and 0.731, respectively. Notably, models trained on coarse-resolution data achieve higher predictive accuracy and exhibit lower uncertainty. In addition, VIs related to the near-infrared and shortwave infrared have a higher importance in FCCF estimation. This framework demonstrates the effectiveness of integrating UAV and satelite data for regional-scale FCCF estimation and provides practical foundation for improving forest monitoring, management decision and accurate carbon sequestration assessment in Chinese fir plantations.
Keyword ：
Fractional cover of Chinese fir (FCCF) Fractional cover of Chinese fir (FCCF) machine learning (ML) machine learning (ML) sentinel imagery sentinel imagery SHapley additive exPlanations (SHAP) SHapley additive exPlanations (SHAP) unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Zhang, Houxi , Cai, Jianwei , Cai, Minghui et al. An upscaling framework for estimating regional-scale fractional cover of Chinese fir (FCCF) by integrating UAV and satellite images [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Zhang, Houxi et al. "An upscaling framework for estimating regional-scale fractional cover of Chinese fir (FCCF) by integrating UAV and satellite images" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Zhang, Houxi , Cai, Jianwei , Cai, Minghui , Chen, Xunlong , Sun, Yiming , Chen, Kun et al. An upscaling framework for estimating regional-scale fractional cover of Chinese fir (FCCF) by integrating UAV and satellite images . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model's ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The AP0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {AP}_{0.5}$$\end{document} reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall mAP0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mAP}_{0.5}$$\end{document} improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.
Keyword ：
Highland barley Highland barley Lightweight Lightweight Object detection Object detection UAV UAV YOLOv5 YOLOv5
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| GB/T 7714 | Cai, Minghui , Deng, Hui , Cai, Jianwei et al. Lightweight highland barley detection based on improved YOLOv5 [J]. | PLANT METHODS , 2025 , 21 (1) . |
| MLA | Cai, Minghui et al. "Lightweight highland barley detection based on improved YOLOv5" . | PLANT METHODS 21 . 1 (2025) . |
| APA | Cai, Minghui , Deng, Hui , Cai, Jianwei , Guo, Weipeng , Hu, Zhipeng , Yu, Dongzheng et al. Lightweight highland barley detection based on improved YOLOv5 . | PLANT METHODS , 2025 , 21 (1) . |
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The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty.
Keyword ：
Fujian province Fujian province multidimensional poverty index (MPI) multidimensional poverty index (MPI) nighttime light (NL) nighttime light (NL) random forest (RF) random forest (RF)
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| GB/T 7714 | Zheng, Xiaoqian , Zhang, Wenjiang , Deng, Hui et al. County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data [J]. | REMOTE SENSING , 2024 , 16 (6) . |
| MLA | Zheng, Xiaoqian et al. "County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data" . | REMOTE SENSING 16 . 6 (2024) . |
| APA | Zheng, Xiaoqian , Zhang, Wenjiang , Deng, Hui , Zhang, Houxi . County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data . | REMOTE SENSING , 2024 , 16 (6) . |
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The ecosystem of the Qinghai-Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models-Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)-to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai-Tibet Plateau, including Salix psammophila, Populus simonii Carri & egrave;re, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carri & egrave;re, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation.
Keyword ：
feature optimization feature optimization OBIA OBIA Tibetan Plateau Tibetan Plateau UAV remote sensing UAV remote sensing vegetation classification vegetation classification
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| GB/T 7714 | Chen, Xiaoqiang , Deng, Hui , Zhang, Wenjiang et al. A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery [J]. | REMOTE SENSING , 2024 , 16 (21) . |
| MLA | Chen, Xiaoqiang et al. "A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery" . | REMOTE SENSING 16 . 21 (2024) . |
| APA | Chen, Xiaoqiang , Deng, Hui , Zhang, Wenjiang , Zhang, Houxi . A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery . | REMOTE SENSING , 2024 , 16 (21) . |
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Moso bamboo (Phyllostachys edulis) forest is a key ecosystem and its soil microbial community plays a crucial role in maintaining the ecosystem's functions, but it is very vulnerable to climate change. An altitude gradient can positively simulate environmental conditions caused by climate change, and hence, it provides an efficient means of investigating the response of soil microorganisms to such climatic changes. However, while previous research has largely concentrated on plant-soil-microorganism interactions across broad altitudinal ranges encompassing multiple vegetation types, studies examining these interactions within a single ecosystem across small altitudinal gradients remain scarce. This study took Moso bamboo forests at different altitudes in Wuyi Mountain, China, as the research object and used high-throughput sequencing technology to analyze the soil microbial community structure, aiming to elucidate the changes in soil microbial communities along the altitude gradient under the same vegetation type and its main environmental driving factors. This study found that the structure of bacterial community was notably different in Moso bamboo forests' soil at varying altitudes, unlike the fungal community structure, which showed relatively less variance. Bacteria from Alphaproteobacteria phylum were the most dominant (14.71-22.91%), while Agaricomycetes was the most dominating fungus across all altitudinal gradients (18.29-30.80%). Fungal diversity was higher at 530 m and 850 m, while bacterial diversity was mainly concentrated at 850 m and 1100 m. Redundancy analysis showed that soil texture (sand and clay content) and available potassium content were the main environmental factors affecting fungal community structure, while clay content, pH, and available potassium content were the main drivers of bacterial community structure. This study demonstrates that the altitude gradient significantly affects the soil microbial community structure of Moso bamboo forest, and there are differences in the responses of different microbial groups to the altitude gradient. Soil properties are important environmental factors that shape microbial communities. The results of this study contribute to a deeper understanding of the impact of altitude gradient on the soil microbial community structure of Moso bamboo forests, thus providing support for sustainable management of Moso bamboo forests under climate change scenarios.
Keyword ：
altitudes altitudes microbial diversity microbial diversity Moso bamboo forest Moso bamboo forest soil microbial community structure soil microbial community structure
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| GB/T 7714 | Sun, Yiming , Chen, Xunlong , Cai, Jianwei et al. Altitudinal Effects on Soil Microbial Diversity and Composition in Moso Bamboo Forests of Wuyi Mountain [J]. | PLANTS-BASEL , 2024 , 13 (17) . |
| MLA | Sun, Yiming et al. "Altitudinal Effects on Soil Microbial Diversity and Composition in Moso Bamboo Forests of Wuyi Mountain" . | PLANTS-BASEL 13 . 17 (2024) . |
| APA | Sun, Yiming , Chen, Xunlong , Cai, Jianwei , Li, Yangzhuo , Zhou, Yuhan , Zhang, Houxi et al. Altitudinal Effects on Soil Microbial Diversity and Composition in Moso Bamboo Forests of Wuyi Mountain . | PLANTS-BASEL , 2024 , 13 (17) . |
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The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1-S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.
Keyword ：
crop classification crop classification multispectral images multispectral images random forest (RF) random forest (RF) segmentation algorithm segmentation algorithm unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Deng, Hui , Zhang, Wenjiang , Zheng, Xiaoqian et al. Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image [J]. | AGRICULTURE-BASEL , 2024 , 14 (4) . |
| MLA | Deng, Hui et al. "Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image" . | AGRICULTURE-BASEL 14 . 4 (2024) . |
| APA | Deng, Hui , Zhang, Wenjiang , Zheng, Xiaoqian , Zhang, Houxi . Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image . | AGRICULTURE-BASEL , 2024 , 14 (4) . |
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Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R2 values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R2 of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology.
Keyword ：
fractional vegetation cover (FVC) fractional vegetation cover (FVC) machine learning (ML) machine learning (ML) multi-scale satellite multi-scale satellite uncrewed aerial vehicle (UAV) uncrewed aerial vehicle (UAV)
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| GB/T 7714 | Chen, Xunlong , Sun, Yiming , Qin, Xinyue et al. Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning [J]. | REMOTE SENSING , 2024 , 16 (19) . |
| MLA | Chen, Xunlong et al. "Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning" . | REMOTE SENSING 16 . 19 (2024) . |
| APA | Chen, Xunlong , Sun, Yiming , Qin, Xinyue , Cai, Jianwei , Cai, Minghui , Hou, Xiaolong et al. Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning . | REMOTE SENSING , 2024 , 16 (19) . |
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本实用新型公开了可变坡的微型径流小区，包括填土土槽箱本体与稳定底板连接架，所述填土土槽箱本体一端的内侧固定连接有径流收集平台，所述填土土槽箱本体一端的内侧活动连接有翻盖，所述翻盖位于径流收集平台的上端，所述稳定底板连接架的上端均定位有千斤顶和固定支架，所述千斤顶位于固定支架的一端。本实用新型所述的可变坡的微型径流小区，具有可重现性好的效果，且可以满足人工降水的试验条件，并且生产和使用成本相对较低，可以完成限制因素研究，单独量化坡度，并且除了千斤顶外，其他的都是不锈钢材质，也加强了防腐效果，延长使用的寿命，通过调节千斤顶实现调节坡度的过程。
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| GB/T 7714 | 杨凯杰 , 张厚喜 , 刘海立 et al. 可变坡的微型径流小区 : CN202323113278.3[P]. | 2023-11-17 . |
| MLA | 杨凯杰 et al. "可变坡的微型径流小区" : CN202323113278.3. | 2023-11-17 . |
| APA | 杨凯杰 , 张厚喜 , 刘海立 , 何燕 , 严鸿林 . 可变坡的微型径流小区 : CN202323113278.3. | 2023-11-17 . |
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Weeds have a significant impact on the growth of rice. Accurate information about weed infestations can provide farmers with important information to facilitate the precise use of chemicals. In this study, we utilized visible light images captured by UAVs to extract information about weeds in areas of two densities on farmland. First, the UAV images were segmented using an optimal segmentation scale, and the spectral, texture, index, and geometric features of each segmented object were extracted. Cross-validation and recursive feature elimination techniques were combined to reduce the dimensionality of all features to obtain a better feature set. Finally, we analyzed the extraction effect of different feature dimensions based on the random forest (RF) algorithm to determine the best feature dimensions, and then we further analyzed the classification result of machine learning algorithms, such as random forest, support vector machine (SVM), decision tree (DT), and K-nearest neighbors (KNN) and compared them based on the best feature dimensions. Using the extraction results of the best classifier, we created a zoning map of the weed infestations in the study area. The results indicated that the best feature subset achieved the highest accuracy, with respective overall accuracies of 95.38% and 91.33% for areas with dense and sparse weed densities, respectively, and F1-scores of 94.20% and 90.57. Random forest provided the best extraction results for each machine learning algorithm in the two experimental areas. When compared to the other algorithms, it improved the overall accuracy by 1.74-12.14% and 7.51-11.56% for areas with dense and sparse weed densities, respectively. The F1-score improved by 1.89-17.40% and 7.85-10.80%. Therefore, the combination of object-based image analysis (OBIA) and random forest based on UAV remote sensing accurately extracted information about weeds in areas with different weed densities for farmland, providing effective information support for weed management.
Keyword ：
object-based image analysis object-based image analysis random forest random forest UAV remote sensing UAV remote sensing weeds weeds
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| GB/T 7714 | Feng, Chao , Zhang, Wenjiang , Deng, Hui et al. A Combination of OBIA and Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland [J]. | REMOTE SENSING , 2023 , 15 (19) . |
| MLA | Feng, Chao et al. "A Combination of OBIA and Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland" . | REMOTE SENSING 15 . 19 (2023) . |
| APA | Feng, Chao , Zhang, Wenjiang , Deng, Hui , Dong, Lei , Zhang, Houxi , Tang, Ling et al. A Combination of OBIA and Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland . | REMOTE SENSING , 2023 , 15 (19) . |
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实时准确的蔬菜种植信息是实现水肥精准管理和产量准确估算的重要基础。对无人机多光谱影像进行分割，以光谱特征（spectrum features,SPEC）为基础，分别引入指数特征（index features,INDE）、纹理特征（grey-level co-occurrence matrix features,GLCM）和几何特征（geometric features,GEOM）构建8个分类方案（S1～S8），使用随机森林算法进行分类并分析分类效果。结果表明，方案S5(SPEC+GLCM+INDE)的分类效果最好，总体精度和Kappa系数分别为92.75%和0.92。几何特征的引入降低了分类精度，而纹理和指数特征则与其相反；仅依靠光谱、指数和纹理特征仍难以有效区分白菜和包菜，为提高精度后续研究有必要引入植株高度等特征；在4大类特征中，重要性排在首位的是光谱特征，其次为指数特征。基于无人机多光谱影像和随机森林算法能获得较高的蔬菜分类精度，并能确认影响精度的重要特征，可为其他作物的精准识别提供借鉴。
Keyword ：
多光谱影像 多光谱影像 无人机 无人机 蔬菜 蔬菜 随机森林 随机森林 面向对象 面向对象
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| GB/T 7714 | 郭倩 , 魏嘉豪 , 张健 et al. 基于无人机多光谱影像和随机森林的蔬菜识别 [J]. | 中国农业科技导报 , 2023 , 25 (02) : 99-110 . |
| MLA | 郭倩 et al. "基于无人机多光谱影像和随机森林的蔬菜识别" . | 中国农业科技导报 25 . 02 (2023) : 99-110 . |
| APA | 郭倩 , 魏嘉豪 , 张健 , 叶章熙 , 张厚喜 , 赖正清 et al. 基于无人机多光谱影像和随机森林的蔬菜识别 . | 中国农业科技导报 , 2023 , 25 (02) , 99-110 . |
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