Remote Sensing Extraction of Crown Planar Area and Plant Number of Papayas Using UAV Images with Very High Spatial Resolution
文献类型: 外文期刊
作者: Shuangshuang Lai;Hailin Ming;Qiuyan Huang;Zhihao Qin;Lian Duan;Fei Cheng;Guangping Han
作者机构:
关键词: crown planar area extraction;low-pass filter;mean–standard deviation threshold;Otsu’s method;plant number extraction;remote sensing of papaya orchard
期刊名称: Agronomy
ISSN: 2073-4395
年卷期: 2024 年 14 卷 3 期
页码:
收录情况: SCIE(2024版)
摘要: The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu’s method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology.
分类号:
- 相关文献
作者其他论文 更多>>
-
Multi-Omics Integration Reveals Key Genes, Metabolites and Pathways Underlying Meat Quality and Intramuscular Fat Deposition Differences Between Tibetan Pigs and Duroc × Tibetan Crossbred Pigs
作者:Junda Wu;Qiuyan Huang;Baohong Li;Zixiao Qu;Xinming Li;Fei Li;Haiyun Xin;Jie Wu;Chuanhuo Hu;Sen Lin;Xiangxing Zhu;Dongsheng Tang;Chuang Meng;Zongliang Du;Erwei Zuo;Fanming Meng;Sutian Wang
关键词:IMF deposition;local pig breeds;multi-omics;pork quality
-
Joint optimization of AI large and small models for surface temperature and emissivity retrieval using knowledge distillation
作者:Wang Dai;Kebiao Mao;Zhonghua Guo;Zhihao Qin;Jiancheng Shi;Sayed M. Bateni;Liurui Xiao
关键词:Artificial intelligence;Automated machine learning;Knowledge distillation;Large models;Remote sensing parameter retrieval
-
AI-Based Downscaling of MODIS LST Using SRDA-Net Model for High-Resolution Data Generation
作者:Hongxia Ma;;Kebiao Mao;;Zijin Yuan;;Longhao Xu;;Jiancheng Shi;;Zhonghua Guo;;Zhihao Qin
关键词:land surface temperature (LST); downscaling; MODIS; SRDA-Net
-
Reconstruction of cloudy land surface temperature by combining surface energy balance theory and solar-cloud-satellite geometry
作者:Wenhui Du;;;;Zhao-Liang Li;;;;Zhihao Qin;;;;Jinlong Fan;;;;Xiangyang Liu;;;;Chunliang Zhao;;;;Kun Cao
关键词:Land surface temperature (LST) under clouds;;;;reconstruction;;;;solar-cloud-satellite geometry;;;;surface energy balance (SEB)
-
Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images
作者:Qiuyan Huang;Juanjuan Feng;Maofang Gao;Shuangshuang Lai;Guangping Han;Zhihao Qin;Jinlong Fan;Yuling Huang
关键词:allometric variables;crop canopy surface model;crop yield estimation;sugarcane farming;UAV remote sensing
-
Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
作者:Mi Luo;Shoaib Ahmad Anees;Qiuyan Huang;Xin Qin;Zhihao Qin;Jianlong Fan;Guangping Han;Liguo Zhang;Helmi Zulhaidi Mohd Shafri
关键词:above-ground biomass;CatBoost;ensemble model;machine learning
-
Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China
作者:Qiaomei Su;Jin Lv;Jinlong Fan;Weili Zeng;Rong Pan;Yuejiao Liao;Ying Song;Chunliang Zhao;Zhihao Qin;Pierre Defourny
关键词:classification;GF-1;irrigation fields;irrigation map