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Sensors 2018, 18(3), 708; https://doi.org/10.3390/s18030708

Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields

1
Department of Geography-Hutt Building, University of Guelph, Guelph, ON N1G 2W1, Canada
2
Agriculture and Agri-Food Canada (AAFC)-Science and Technology Branch, 174 Stone Road West, Guelph, ON N1G 4S9, Canada
3
FieldTRAKS Solutions Inc., 6367 McCordick Road, North Gower, Ottawa, ON K0A 2T0, Canada
*
Author to whom correspondence should be addressed.
Received: 15 January 2018 / Revised: 20 February 2018 / Accepted: 21 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Sensors in Agriculture)
Full-Text   |   PDF [3225 KB, uploaded 28 February 2018]   |  

Abstract

Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming. View Full-Text
Keywords: agricultural land; field crops; land cover; photograph-grid method; remote sensing; data validation and calibration; mobile app agricultural land; field crops; land cover; photograph-grid method; remote sensing; data validation and calibration; mobile app
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Laamrani, A.; Pardo Lara, R.; Berg, A.A.; Branson, D.; Joosse, P. Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields. Sensors 2018, 18, 708.

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