Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems

Hunt, E. Raymond; Rondon, Silvia I.; Hamm, Philip B.; Turner, Robert; Bruce, Alan E.; Brungardt, Josh J.

doi:10.1117/12.2224139

Cited by 12 publications

(10 citation statements)

References 28 publications

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“…One study on UAS-based detection of potato beetle infestation found no correlation between plot-scale vegetation indices, but it was able to detect insect damage using object-based feature extraction. 24 A significant negative correlation has been found between plant injury rating due to aphids and NDVI in grain sorghum fields using an airborne multispectral imaging system. 25 Unfortunately, aerial detection of plant stress may require substantial, large-scale plant stress after the window of deploying a mitigation measure has passed.…”

Section: Introductionmentioning

confidence: 95%

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: Introductionmentioning

confidence: 95%

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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“…Focusing on the UAV technologies being used for PA, the types of Unmanned Aerial Vehicles can be divided into five basic categories, based on their design characteristics (see Figure 1). (d) blimp [123]; (e) flapping-wing (SmartBird) [122]; and (f) parafoil-wing (Tetracam) [90].…”

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confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…To gather information for PA, several sensor platforms, including satellites and aircraft, have been used. Nevertheless, the platforms are not wholly adequate to provide information at the required spatial and time resolutions, as the images taken from them are expensive, and they can be affected by weather conditions such as clouds (Ehsani, Sankaran, Maja, & Camargo Neto, 2014;Hunt et al, 2014). Even the latest generation of very high resolution satellite images (e.g., GeoEye-1 and WorldView-2) is not able to provide high frequency data for critical situations such as monitoring nutrients or water stresses, diseases, or pest attacks.…”

Section: Introductionmentioning

confidence: 99%

“…After colour reconstruction, the green, red and near infrared (G, R, NIR) spectral bands approximately covered the following wavelength intervals: 520e570 nm (G), 600e690 nm (R) and 750e850 nm (NIR). Hunt et al (2014) found that a source of error in this process is the colour correction matrix that is included in the Tetracam PixelWrench software because the best matrix cannot be determined if the spectral response function of the camera sensor is not known. These researchers concluded that sensor calibration is important when comparing data acquired on different dates or from different locations.…”

mentioning

confidence: 99%

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Agüera-Vega

Ramírez

Saiz

et al. 2015

Biosystems Engineering

171

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Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems

Cited by 12 publications

References 28 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

A Review on UAV-Based Applications for Precision Agriculture

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

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