Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images

Chen, Ruizhi; Chu, Tianguang; Landivar, Juan; Yang, Chenghai; Maeda, Murilo M.

doi:10.1007/s11119-017-9508-7

Cited by 57 publications

(53 citation statements)

References 22 publications

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“…This claim is supported by other studies in the literature that show the potential of UASbased photogrammetric approaches for crop growth monitoring and phenotyping. [33][34][35][36] Furthermore, UAS provides orders of magnitude increase in data collection efficiency at the field-scale relative to manual ground observation approaches. For example, the UAS-SfM approach can provide "plant level" crop height measurements across entire fields at daily intervals (see Fig.…”

Section: Discussionmentioning

confidence: 99%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

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"Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment," J. Appl. Remote Sens. 11(2), 026035 (2017), doi: 10.1117/1.JRS.11.026035. Abstract. A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

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Section: Discussionmentioning

confidence: 99%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

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“…1, Fig. 1) Chen et al (2018). The accuracy of water detection at a large scale -using VHR RGB orthophotomaps -is high in comparison to Rokni et al (2014) and Jones (2015) (Tab.…”

Section: Discussionmentioning

confidence: 84%

Free water table area monitoring on wetlands using satellite and UAV orthophotomaps – Kampinos National Park case study

Góraj

Wróblewski

Ciężkowski

et al. 2019

Meteorol. Hydrol. Water Manage.

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The surface water table level is a crucial factor for the existence of wetland habitats, and valuable from the point of view of environmental protection. In particular, surface water table in a hydrological year play an important role, affecting the seasonal changes in conditions of the development of species inhabiting a given patch of vegetation. The occurrence of floods often determines the possibility of survival of a given plant community. Information on the seasonal variability of surface waters, and above all the range of seasonal floods, is very important from the point of view of planning protection activities in National Parks in order to preserve wetland habitats. Nowadays, remote sensing data is an important source of spatial information, particularly those characterized by low cost data acquisition and processing. One such source is imagery collected from satellites, along with products freely distributed by the European Space Agency. Satellites of the Sentinel constellation provide multi-spectral optical remote sensing images recorded at visible and infrared wavelengths. Due to the short satellite revisit time of the Sentinel, the images from this satellite constitute a potential source of information for the monitoring of moisture on wetlands with a high temporal resolution. In this study, the authors aim to demonstrate the possibilities associated with the use of satellite images to monitor the range of a free surface water table in the pilot area located within the basin of the Łasica Channel, located in the Kampinos National Park (Poland). The accuracy of the results of the remote sensing transformations will be assessed using high resolution RGB images obtained with the use of unmanned aerial vehicles (UAV) and control points measurements. The maps of free water table has been acquired as an result of ensemble regressors (Random Forest, Extra Trees, Bagging). Regressors has been learned and applied for two sessions. Promising results were obtained indicating the possibility of using the proposed method on a similar scale.

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“…Although calibration is usually required, there are a number of biophysical parameters that may be measured with less calibration, including certain variables related to phenology, growth, leaf area, height, and biomass traits, that in turn correlate with productivity and yields (Chen, Chu, Landivar, Yang, & Maeda, 2018;Dempewolf, Nagol, Hein, Thiel, & Zimmermann, 2017;Díaz-Varela et al, 2015;Granados, Bonnet, Hansen, & Schmidt, 2013;Swain, Thomson, & Jayasuriya, 2010;Torres-Sánchez, López-Granados, Serrano, Arquero, & Peña, 2015). By flying closer to the plant, MAVs and NAVs increase the possibilities for measuring previously unseen variables that are important for precision agriculture and phenotyping and, in doing so, further minimize calibration requirements.…”

Section: Flying Closer To the Target: Navs And Mavs Open New Opportunmentioning

confidence: 99%

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

et al. 2020

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By collecting data at spatial and temporal scales that are inaccessible to satellite and field observation, unmanned aerial vehicles (UAVs) are revolutionizing a number of scientific and management disciplines. UAVs may be particularly valuable for precision agricultural applications, offering strong potential to improve the efficiency of water, nutrient, and disease management. However, some authors have suggested that the UAV industry has overhyped the potential value of this technology for agriculture, given that it is difficult for non‐specialists to operate UAVs as well as to process and interpret the resulting data. Here, we analyze the barriers to applying UAVs for precision agriculture, which range from regulatory issues to technical requirements. We then evaluate how new developments in the nano‐ and micro‐UAV (NAV and MAV, respectively) markets may help to overcome these barriers. Among the possible breakthroughs that we identify is the ability of NAV/MAV platforms to directly quantify plant traits using methods (e.g., object‐oriented classification) that require less image calibration and interpretation than spectral index–based approaches. We suggest that this potential, when combined with steady improvements in sensor miniaturization, flight precision, and autonomy as well as cloud‐based image processing, will make UAVs a tool with much broader adoption by agricultural managers in the near future. If this wider uptake is realized, then UAVs have real potential to improve agriculture's resource‐use efficiency. © 2020 by John Wiley & Sons, Inc.

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Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images

Cited by 57 publications

References 22 publications

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Free water table area monitoring on wetlands using satellite and UAV orthophotomaps – Kampinos National Park case study

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

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