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

Authors:
  • Mohammed VI Polytechnic University (UM6P)

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. Keywords: agricultural land; field crops; land cover; photograph-grid method; remote sensing; data validation and calibration; mobile app
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... The independent classifications were then combined, and final labeling based on simple majority rule applied (no ties occurred). Automated methods, such as those described in [29], could be implemented. However, these approaches are typically not applied to the oblique camera views used in this study. ...
... This field data was obtained either the same day or within one day of the roadside image capture. The in-field research sites were evaluated for soil cover using a photo-grid sampling technique where average counts were derived from three digital images captured at 90 degrees from surface normal, or nadir view, using 0.75 x 1.0 meter survey quadrats over a 30 m x 30 m area; the three photo locations were selected to represent the low, medium, and high range of soil cover in the field [29]. Residue and green cover counts of the quadrat photos were performed using 10 x 10 digital grids, representing 100 points for each photo imaging frame (e.g., left side of Figure 4 [3,4,29], where cover percentage was based on presence or absence over each of the 100 grid intersection points, and categorized as CV (0%-30%); CS (30-60%); and NT (60%-100%). ...
... The in-field research sites were evaluated for soil cover using a photo-grid sampling technique where average counts were derived from three digital images captured at 90 degrees from surface normal, or nadir view, using 0.75 x 1.0 meter survey quadrats over a 30 m x 30 m area; the three photo locations were selected to represent the low, medium, and high range of soil cover in the field [29]. Residue and green cover counts of the quadrat photos were performed using 10 x 10 digital grids, representing 100 points for each photo imaging frame (e.g., left side of Figure 4 [3,4,29], where cover percentage was based on presence or absence over each of the 100 grid intersection points, and categorized as CV (0%-30%); CS (30-60%); and NT (60%-100%). For this experiment, the green cover class was assigned when >90% field was visually composed of green, actively photosynthesizing vegetation which was representative of the spring season (May); however, different classifications of green cover may be desired if the survey was conducted in the fall when green cover would be less established. ...
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... Studies [10] show that mobile devices equipped with a camera and applications can perform an adequate digital analysis of crop residues, especially for fields with a low level of residues (less than 30% of the surface), i.e. in the case of traditional tillage. ...
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