Early Stress Detection in Plant Phenotyping using CNN and LSTM Architecture

Author(s):  
Krit Rojanarungruengporn ◽  
Suree Pumrin
2020 ◽  
Vol 13 (1) ◽  
pp. 68
Author(s):  
Mónica Pineda ◽  
Matilde Barón ◽  
María-Luisa Pérez-Bueno

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.


2011 ◽  
Vol 115 (12) ◽  
pp. 3640-3646 ◽  
Author(s):  
Jan U.H. Eitel ◽  
Lee A. Vierling ◽  
Marcy E. Litvak ◽  
Dan S. Long ◽  
Urs Schulthess ◽  
...  

Author(s):  
V. Sagan ◽  
M. Maimaitijiang ◽  
P. Sidike ◽  
M. Maimaitiyiming ◽  
H. Erkbol ◽  
...  

<p><strong>Abstract.</strong> Early stress detection is critical for proactive field management and terminal yield prediction, and can aid policy making for improved food security in the context of climate change and population growth. Field surveys for crop monitoring are destructive, labor-intensive, time-consuming and not ideal for large-scale spatial and temporal monitoring. Recent technological advances in Unmanned Aerial Vehicle (UAV) and high-resolution satellite imaging with frequent revisit time have proliferated the applications of this emerging new technology in precision agriculture to address food security challenges from regional to global scales. In this paper, we present a concept of UAV and satellite virtual constellation to demonstrate the power of multi-scale imaging for crop monitoring. Low-cost sensors integrated on a UAV were used to collect RGB, multispectral, and thermal images during the growing season in a test site established near Columbia, Missouri, USA. WorldView-3 multispectral data were pan-sharpened, atmospherically corrected to reflectance and combined with UAV data for temporal monitoring of early stress. UAV thermal and multispectral data were calibrated to canopy temperature and reflectance following a rigorous georeferencing and ortho-correction. The results show that early stress can be effectively detected using multi-temporal and multi-scale UAV and satellite observation; the limitations of satellite remote sensing data in field-level crop monitoring can be overcome by using low altitude UAV observations addressing not just mixed pixel issues but also filling the temporal gap in satellite data availability enabling capture of early stress. The concept developed in this paper also provides a framework for accurate and robust estimation of plant traits and grain yield and delivers valuable insight for high spatial precision in high-throughput phenotyping and farm field management.</p>


2019 ◽  
Vol 11 (3) ◽  
pp. 330 ◽  
Author(s):  
Vasit Sagan ◽  
Maitiniyazi Maimaitijiang ◽  
Paheding Sidike ◽  
Kevin Eblimit ◽  
Kyle Peterson ◽  
...  

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.


1967 ◽  
Author(s):  
W. F. ANGERMEIER ◽  
ROBERT NAY ◽  
ROBERT DAVIS
Keyword(s):  

2008 ◽  
Author(s):  
Lindsay A. Metcalfe ◽  
Elizabeth A. Harvey ◽  
John H. Fanton ◽  
Dhara Thakar ◽  
Sharonne Herbert

Sign in / Sign up

Export Citation Format

Share Document