scholarly journals UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

2019 ◽  
Vol 11 (3) ◽  
pp. 330 ◽  
Author(s):  
Vasit Sagan ◽  
Maitiniyazi Maimaitijiang ◽  
Paheding Sidike ◽  
Kevin Eblimit ◽  
Kyle Peterson ◽  
...  
Keyword(s):  
Image Quality ◽  
Precision Agriculture ◽  
Thermal Imaging ◽  
The Other ◽  
Plant Phenotyping ◽  
Reference Image ◽  
Phenotypic Traits ◽  
Stress Detection ◽  
Biochemical Traits ◽  
Thermal Cameras

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivityand atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

scholarly journals Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

2021 ◽  
Vol 13 (17) ◽  
pp. 3517
Author(s):  
Rui Xu ◽  
Changying Li ◽  
Sergio Bernardes
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Precision Agriculture ◽  
Canopy Height ◽  
Cotton Field ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Unmanned Aerial System ◽  
Data Acquisition Software ◽  
Acquisition Software

Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

scholarly journals EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping

2021 ◽  
Vol 13 (13) ◽  
pp. 2622
Author(s):  
Haozhou Wang ◽  
Yulin Duan ◽  
Yun Shi ◽  
Yoichiro Kato ◽  
Seish Ninomiya ◽  
...  
Keyword(s):  
Data Processing ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Data Annotation ◽  
Intermediate Data ◽  
Reconstruction Quality ◽  
Open Source Software Package ◽  
Machine Learning Applications ◽  
Data Processor ◽  
Uav Images

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include 1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and 2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

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