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

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

Department of Geography-Hutt Building, University of Guelph, Guelph, ON N1G 2W1, Canada
Agriculture and Agri-Food Canada (AAFC)-Science and Technology Branch, 174 Stone Road West, Guelph, ON N1G 4S9, Canada
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)
View Full-Text   |   Download PDF [3225 KB, uploaded 28 February 2018]   |  


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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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