Crop specific LAI retrieval using optical and radar satellite data for regional crop growth monitoring and modelling

2005 ◽  
Author(s):  
Vincent Guissard ◽  
Cozmin Lucau-Danila ◽  
Pierre Defourny
Keyword(s):  
Satellite Data ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Radar Satellite

User requirements for a satellite-based advisory platform

2017 ◽  
Vol 8 (2) ◽  
pp. 368-371 ◽  
Author(s):  
E. Anastasiou ◽  
Z. Tsiropoulos ◽  
S. Fountas ◽  
A. Osann ◽  
D. Protic ◽  
...  
Keyword(s):  
Satellite Data ◽  
Historical Data ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Growth Monitoring ◽  
User Requirements ◽  
Small Farmers ◽  
Scheduling Irrigation ◽  
Smart Agriculture ◽  
Eu Project

APOLLO, a newly funded H2020 EU project will develop an agricultural advisory platform for small farmers based on Copernicus Sentinel satellites. It will provide services for tillage scheduling, irrigation scheduling, crop growth monitoring and yield estimation. The aim of this study was to identify the farmers’ requirements of the APOLLO platform. In total 121 farmers were interviewed in Spain, Serbia and Greece. More than 90% of the farmers pointed out that smart agriculture and use of satellite data in agriculture are important. Additionally, more than 80% want to have access to historical data and a flexible subscription policy to the platform according to their needs and use. However, significant differences exist among farmers of these countries in terms of technology awareness and penetration, which should be taken into consideration for developing a successful platform.

scholarly journals Design and Testing of an Active Light Source Apparatus for Crop Growth Monitoring and Diagnosis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 206474-206490
Author(s):  
Lili Yao ◽  
Rusong Wu ◽  
Shun Wu ◽  
Xiaoping Jiang ◽  
Yan Zhu ◽  
...  
Keyword(s):  
Light Source ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Monitoring And Diagnosis ◽  
Design And Testing

scholarly journals Development of mobile RoboVision with stereo camera for automatic crop growth monitoring in plant factory

2019 ◽  
Author(s):  
Anggit Wijanarko ◽  
Andri Prima Nugroho ◽  
Lilik Sutiarso ◽  
Takashi Okayasu
Keyword(s):  
Crop Growth ◽  
Growth Monitoring ◽  
Stereo Camera ◽  
Plant Factory

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

scholarly journals Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth Monitoring: A Case Study in China

2019 ◽  
Vol 11 (7) ◽  
pp. 809 ◽  
Author(s):  
Lijuan Wang ◽  
Guimin Zhang ◽  
Ziyi Wang ◽  
Jiangui Liu ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Crop Growth ◽  
Future Trend ◽  
Current Status ◽  
Research Trend ◽  
Growth Monitoring ◽  
Knowledge Mapping ◽  
Area Index ◽  
Quantitative Visualization

Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as “vegetation index”, “crop growth”, “winter wheat”, “leaf area index (LAI)”, and “model” in the field of crop growth monitoring, and “LAI” and “unmanned aerial vehicle (UAV)”, appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China.

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