UAV-based thermal imaging in the assessment of water status of soybean plants

Crusiol, Luís Guilherme Teixeira; Nanni, Marcos Rafael; Furlanetto, Renato Herrig; Sibaldelli, R. N. R.; Cézar, Everson; Mertz-Henning, L. M.; Nepomuceno, Alexandre Lima; Neumaier, N.; Farias, J. R. B.

doi:10.1080/01431161.2019.1673914

Cited by 58 publications

(38 citation statements)

References 37 publications

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“…Pest and disease infestations are often associated with the unfavourable moisture conditions of soil and vegetation, i.e., excess water or drought. In contrast with weed, pest, or disease detection, soil moisture regime-related UAV applications are commonly based on the thermal region of the spectra [104,105], although visible reflectance [106] or multispectral data [107] are also widely used. Thermal imaging was not considered in the reviewed articles.…”

Section: Discussionmentioning

confidence: 99%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Section: Discussionmentioning

confidence: 99%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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“…As an imaging technique, thermography possess the advantage to acquire geolocated data at canopy scale, overcoming the classic handheld infrared thermometers, applied at leaf or single plant scales (Crusiol et al, 2020 ), providing results on a whole plant basis (Poirier-Pocovi et al, 2020 ). TIR cameras are widely used as portable devices for both Unmanned Aerial Vehicles (UAVs) (Sagan et al, 2019 ) or for agricultural vehicle proximal to the ground, as an on-the-go system, or even involving machine learning (Gutirrez et al, 2018 ), characterized by low-cost data acquisition, easy implementation, processing and immediate response as well as higher spatial-, spectral- and temporal resolutions.…”

Section: Remote Sensing Qualitative Methods For Stress Assessmentmentioning

confidence: 99%

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Galieni¹,

et al. 2021

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Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

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“…In addition, future drone-borne reflectance surveys could be designed to provide a spatial and temporal resolution bridge between multi-dataset orbital measurements, e.g., [16][17][18][19] and ground-based observations. For example, future drone-borne surveys could expand the aircraft sensor suite from SWIR (used in this study) to include visible/nearinfrared, e.g., [45] reflectance imagers alongside thermal infrared sensors [46], which could provide simultaneous NDVI and ground temperature measurements similar to those collected from orbit, e.g., [18,19].…”

Section: Hydrogeological Summary and Future Workmentioning

confidence: 99%

Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas

Levy

Johnson

2021

Remote Sensing

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The extent, timing, and magnitude of soil moisture in wetlands (the hydropattern) is a primary physical control on biogeochemical processes in desert environments. However, determining playa hydropatterns is challenged by the remoteness of desert basin sites and by the difficulty in determining soil moisture from remotely sensed data at fine spatial and temporal scales (hundreds of meters to kilometers, and hours to days). Therefore, we developed a new, reflectance-based soil moisture index (continuum-removed water index, or CRWI) that can be determined via hyperspectral imaging from drone-borne platforms. We compared its efficacy at remotely determining soil moisture content to existing hyperspectral and multispectral soil moisture indices. CRWI varies linearly with in situ soil moisture content (R2 = 0.89, p < 0.001) and is comparatively insensitive to soil clay content (R2 = 0.4, p = 0.01), soil salinity (R2 = 0.82, p < 0.001), and soil grain size distribution (R2 = 0.67, p < 0.001). CRWI is negatively correlated with clay content, indicating it is not sensitive to hydrated mineral absorption features. CRWI has stronger correlation with surface soil moisture than other hyperspectral and multispectral indices (R2 = 0.69, p < 0.001 for WISOIL at this site). Drone-borne reflectance measurements allow monitoring of soil moisture conditions at the Alvord Desert playa test site over hectare-scale soil plots at measurement cadences of minutes to hours. CRWI measurements can be used to determine surface soil moisture at a range of desert sites to inform management decisions and to better reveal ecosystem processes in water-limited environments.

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UAV-based thermal imaging in the assessment of water status of soybean plants

Cited by 58 publications

References 37 publications

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas

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