Using a mobile device “app” and proximal remote sensing technologies to assess soil cover fractions on agricultural fields


Laamrani, A., Lara, R.P., Berg, A.A., Branson, D., Joosse, P. (2018). Using a mobile device “app” and proximal remote sensing technologies to assess soil cover fractions on agricultural fields. Sensors, [online] 18(3),

Plain language summary

As a conservation practice, farmers are encouraged to leave greater than 30% crop residue cover on their fields to reduce erosion and maintain soil organic matter. Conventional methods of measuring residue cover are laborious, involving the counting of marked points on a line in a field or counting residue occurring under grid intersections on photographs taken from fields. Mobile devices are being more widely used on farms in Canada and contain technologies such as cameras and image processors that could enable easier measurement of crop residue cover. In this study, a mobile device app was tested against two grid counting methods using photographs from corn and soybean fields with varying levels of crop residue remaining after harvest. Using the app simply requires taking a photo at shoulder height directly above the ground and then selecting 4 to 8 points on the photo to represent the range in residue colour present. The app then automatically calculates the per cent residue cover in the photo. While simpler to use, the app did tend to underestimate the amount of residue cover determined by the standard photo grid counting methods by 5 to 15% depending on the residue type and amount. The study demonstrated the ability of the app to quickly provide a repeatable measure of residue cover in the field, particular to determine if a field has less than 30% residue, but it is not recommended for research settings where more accurate residue cover measures are required to calibrate or validate remotely sensed data.


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.

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