Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles

2019 ◽  
Vol 113 (2) ◽  
pp. 779-786 ◽  
Author(s):  
Zachary P D Marston ◽  
Theresa M Cira ◽  
Erin W Hodgson ◽  
Joseph F Knight ◽  
Ian V Macrae ◽  
...  

Abstract Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by aphid-induced stress could be detected from ground-based sensors; however, it remained unknown whether these changes could also be detected from UAV-based sensors. Small-plot trials were conducted in 2017 and 2018 where cages were used to manipulate aphid populations. Additional open-field trials were conducted in 2018 where insecticides were used to create a gradient of aphid pressure. Whole-plant soybean aphid densities were recorded along with UAV-based multispectral imagery. Simple linear regressions were used to determine whether UAV-based multispectral reflectance was associated with aphid populations. Our findings indicate that near-infrared reflectance decreased with increasing soybean aphid populations in caged trials when cumulative aphid days surpassed the economic injury level, and in open-field trials when soybean aphid populations were above the economic threshold. These findings provide the first documentation of soybean aphid-induced stress being detected from UAV-based multispectral imagery and advance the use of UAVs for remote scouting of soybean aphid and other field crop pests.

2019 ◽  
Vol 6 ◽  
Author(s):  
Débora Borges ◽  
Isabel Azevedo ◽  
Luís Pádua ◽  
Telmo Adão ◽  
Emanuel Peres ◽  
...  

2021 ◽  
Author(s):  
Massimo Micieli ◽  
Gianluca Botter ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore

<p>UAVs (Unmanned Aerial Vehicles) are increasingly used for monitoring river networks with a broad range of purposes. In this contribution, we focus on the use of multispectral sensors, either in the thermal infrared band LWIR (Long-wavelength infrared, 8-15 µm) or in the infrared band NIR (Near-infrared, 0.75-1.4 µm) to map network dynamics in temporary streams. Specifically, we discuss the first results of a set of surveys carried out in 2020 within a small river catchment located in northern Calabria (southern Italy), as part of the research activities of the ERC-funded DyNET project. Preliminary, a rigorous methodology was identified to perform on-site surveys and to process and analyse the acquired images. Experimental results show that the combined use of LWIR and NIR sensors is a suitable solution for detecting water presence in channels characterized by different hydraulic and morphologic conditions. LWIR sensors alone allow one to discriminate water presence only when the thermal contrast with the surrounding environment is high. On the other hand, NIR sensors permit to detect the presence of water in most of the analyzed settings through the estimate of the Normalized Difference Water Index (NDWI). However, NIR sensors can be misled in case of shallow water depth, due to the NIR radiation emitted by the riverbed merging with that of the water. Overall, the study demonstrates that a combined LWIR/NIR approach allows addressing a broader range of conditions. Moreover, the information provided can be further enhanced by combining it with geomorphologic information and basic hydraulic concepts.</p>


1991 ◽  
Vol 71 (2) ◽  
pp. 385-392 ◽  
Author(s):  
G. B. Schaalje ◽  
H. -H. Mündel

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression


2019 ◽  
Vol 11 (16) ◽  
pp. 1853 ◽  
Author(s):  
Kelly Easterday ◽  
Chippie Kislik ◽  
Todd Dawson ◽  
Sean Hogan ◽  
Maggi Kelly

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.


2019 ◽  
Vol 72 ◽  
pp. 185-194
Author(s):  
Michael R. Trolove ◽  
Paul Shorten

Rapid advancements in UAVs, computing power and photogrammetry techniques now permit low cost biological-monitoring applications using off-the-shelf hardware and software. The utility of four UAV models costing $1,200 - $11, 000 and two photogrammetry programmes were assessed in separate experiments to evaluate their ability to detect standardised plant targets and to generate useable orthomoasic images. The colour and contrast of standardised targets influenced detection by UAVs more than their size as height increased. A large green rosette (50.8 cm2) could be detected by all UAVs from 28–90 m, while a yellow target 13 times smaller could be detected at 36–100 m, with the more expensive UAVs being effective at the higher altitudes. Monitoring vegetation cover or flowering plants is possible at the minimum allowable height altitude of 20 m by all four UAVs. However, identification of species in their vegetative state would require the UAVs with the better camera optics. The two photogrammetry programmes produced suitable orthomosaic images under the pasture, maize and hill country scenarios tested.


2018 ◽  
Vol 10 (11) ◽  
pp. 1812 ◽  
Author(s):  
Chang Cao ◽  
Xuhui Lee ◽  
Joseph Muhlhausen ◽  
Laurent Bonneau ◽  
Jiaping Xu

Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation.


2020 ◽  
Vol 12 (17) ◽  
pp. 2863 ◽  
Author(s):  
L. Minh Dang ◽  
Hanxiang Wang ◽  
Yanfen Li ◽  
Kyungbok Min ◽  
Jin Tae Kwak ◽  
...  

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.


2017 ◽  
Author(s):  
Martin Laurenzis ◽  
Sebastien Hengy ◽  
Alexander Hommes ◽  
Frank Kloeppel ◽  
Alex Shoykhetbrod ◽  
...  

2017 ◽  
Vol 18 (2) ◽  
pp. 95-96 ◽  
Author(s):  
Matthew Wallhead ◽  
Heping Zhu ◽  
John Sulik ◽  
William Stump

Advances in technologies associated with unmanned aerial vehicles (UAVs) have allowed researchers, farmers and agribusinesses to incorporate UAVs coupled with imaging systems into data collection and decision making. Multispectral imagery allows for a quantitative assessment of biophysical indicators or plant health status. What is needed now is a standardized workflow for the quantitative assessment of plant biophysical indicators using low-altitude, high-resolution, UAV-acquired multispectral imagery. With this need in mind, the authors developed and proposed a workflow for extracting plot-level vegetation index means from orthomosaics. As the use of UAVs and associated data collection activities expands, it will become increasingly important that data is being properly incorporated and utilized by ag professionals.


Oryx ◽  
2016 ◽  
Vol 51 (3) ◽  
pp. 513-516 ◽  
Author(s):  
Nathan Hahn ◽  
Angela Mwakatobe ◽  
Jonathan Konuche ◽  
Nadia de Souza ◽  
Julius Keyyu ◽  
...  

AbstractProtected areas across the range of the African savannah elephant Loxodonta africana are increasingly being surrounded and isolated by agriculture and human settlements. Conflicts between people and crop-raiding elephants regularly lead to direct reprisals and diminish community support for conservation. We report on field trials in northern Tanzania that employed a new, humane way for wildlife managers to move elephants away from conflict zones, from distances of > 100 m, thereby enhancing the safety of wildlife managers, farmers and elephants. We deployed 10 unmanned aerial vehicles (drones) piloted by five trained teams of wildlife managers in the Tarangire–Manyara and Serengeti ecosystems. Game Scouts deployed the drones opportunistically during crop-raiding events at the peak of the maize ripening period in 2015 and 2016. In 100% of trials (n = 51) elephants responded to the presence of a drone by departing rapidly from crop fields (n = 38) and settlements (n = 13). The cost of five teams responsible for 617 km2 in Tarangire–Manyara was estimated to be USD 15,520 for 1 year, and all drones remained operational for the duration of the study. The initial success of this tool warrants further testing of the utility of small unmanned aerial vehicles as part of the toolbox for wildlife managers and communities dealing with high levels of conflict with wildlife.


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