{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T10:09:30Z","timestamp":1780049370801,"version":"3.53.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T00:00:00Z","timestamp":1519689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 &amp; 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 \u201capp\u201d 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\u2013grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by \u22126.3% and \u221210.8% 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., \u22125.3% vs. \u22127.4%). For photos with residue &lt;30%, the app derived residue measurements are within \u00b15% 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.<\/jats:p>","DOI":"10.3390\/s18030708","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T14:18:08Z","timestamp":1519741088000},"page":"708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Using a Mobile Device \u201cApp\u201d and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields"],"prefix":"10.3390","volume":"18","author":[{"given":"Ahmed","family":"Laamrani","sequence":"first","affiliation":[{"name":"Department of Geography-Hutt Building, University of Guelph, Guelph, ON N1G 2W1, Canada"},{"name":"Agriculture and Agri-Food Canada (AAFC)-Science and Technology Branch, 174 Stone Road West, Guelph, ON N1G 4S9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7412-1583","authenticated-orcid":false,"given":"Renato","family":"Pardo Lara","sequence":"additional","affiliation":[{"name":"Department of Geography-Hutt Building, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8438-5662","authenticated-orcid":false,"given":"Aaron","family":"Berg","sequence":"additional","affiliation":[{"name":"Department of Geography-Hutt Building, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dave","family":"Branson","sequence":"additional","affiliation":[{"name":"FieldTRAKS Solutions Inc., 6367 McCordick Road, North Gower, Ottawa, ON K0A 2T0, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pamela","family":"Joosse","sequence":"additional","affiliation":[{"name":"Agriculture and Agri-Food Canada (AAFC)-Science and Technology Branch, 174 Stone Road West, Guelph, ON N1G 4S9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/j.rse.2007.08.006","article-title":"Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover","volume":"112","author":"Daughtry","year":"2008","journal-title":"Remote Sens. 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