scholarly journals Easy MPE: Extraction of quality microplot images for UAV-based high-throughput field phenotyping

2019 ◽  
Author(s):  
Léa Tresch ◽  
Yue Mu ◽  
Atsushi Itoh ◽  
Akito Kaga ◽  
Kazunori Taguchi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Automatic Program ◽  
Processing Step ◽  
Field Phenotyping ◽  
Binary Segmentation ◽  
Aerial Vehicle ◽  
Uav Images

AbstractMicroplot extraction (MPE) is a necessary image-processing step in unmanned aerial vehicle (UAV)-based research on breeding fields. At present, it is manually using ArcGIS, QGIS or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semi-automatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1). Binary segmentation, (2). Microplot extraction, (3). Production of *.shp files to enable further file manipulation, and (4). Projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: The average IOU (±SD) of all trials was 91% (±3).

scholarly journals Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Léa Tresch ◽  
Yue Mu ◽  
Atsushi Itoh ◽  
Akito Kaga ◽  
Kazunori Taguchi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Field Conditions ◽  
Processing Step ◽  
Field Phenotyping ◽  
Binary Segmentation ◽  
Aerial Vehicle ◽  
Uav Images

Microplot extraction (PE) is a necessary image processing step in unmanned aerial vehicle- (UAV-) based research on breeding fields. At present, it is manually using ArcGIS, QGIS, or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1) binary segmentation, (2) microplot extraction, (3) production of ∗.shp files to enable further file manipulation, and (4) projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of the proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: the average IOU (±SD) of all trials was 91% (±3).

scholarly journals Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature

2020 ◽  
Vol 11 ◽  
Author(s):  
Gregor Perich ◽  
Andreas Hund ◽  
Jonas Anderegg ◽  
Lukas Roth ◽  
Martin P. Boer ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Canopy Temperature ◽  
Field Phenotyping ◽  
Aerial Vehicle

scholarly journals High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery

2018 ◽  
Vol 151 ◽  
pp. 84-92 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Jianfeng Zhou ◽  
Lav R. Khot ◽  
Jennifer J. Trapp ◽  
Eninka Mndolwa ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Multispectral Imagery ◽  
Dry Bean ◽  
Field Phenotyping ◽  
Aerial Vehicle

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images

2021 ◽  
Vol 173 ◽  
pp. 95-121
Author(s):  
Juepeng Zheng ◽  
Haohuan Fu ◽  
Weijia Li ◽  
Wenzhao Wu ◽  
Le Yu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oil Palm ◽  
Palm Trees ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model

2021 ◽  
Vol 187 ◽  
pp. 106304
Author(s):  
Liang Wan ◽  
Jiafei Zhang ◽  
Xiaoya Dong ◽  
Xiaoyue Du ◽  
Jiangpeng Zhu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Growth Traits ◽  
Field Phenotyping ◽  
Aerial Vehicle ◽  
Crop Biomass
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