Application of artificial neural system algorithms to image analysis of roots in soil, I. initial results
Several back propagation artificial neural system (ANS) architectures were tested to determine their abilities to identify roots in minirhizotron images of soil. The general model had an input level, which consisted of two linear arrays 1 × 11 pixels each, a hidden level, with 7 to 11 nodes, and an...
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| Published in | Geoderma Vol. 53; no. 3; pp. 237 - 253 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.06.1992
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| Online Access | Get full text |
| ISSN | 0016-7061 1872-6259 |
| DOI | 10.1016/0016-7061(92)90057-E |
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| Abstract | Several back propagation artificial neural system (ANS) architectures were tested to determine their abilities to identify roots in minirhizotron images of soil. The general model had an input level, which consisted of two linear arrays 1 × 11 pixels each, a hidden level, with 7 to 11 nodes, and an output level consisting of a single node set to produce a binary root/not-root output. The inputs to the model consisted of two linear arrays, one each from a horizontal and a vertical derivative image produced from the raw image by the Savitzky-Golay algorithm. A training image was constructed by hand-editing the raw image. The back propagation model was trained by repeatedly presenting it with a set of inputs and an associated target response. The error was calculated for the output from each input/target response pair and corrections to the weighting functions were made using a gradient descent back correction algorithm. The results of this study suggest that the ANS approach has potential to identify roots in images of soil. Suggestions for improving the performance of the model are presented. |
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| AbstractList | Several back propagation artificial neural system (ANS) architectures were tested to determine their abilities to identify roots in minirhizotron images of soil. The general model had an input level, which consisted of two linear arrays 1 × 11 pixels each, a hidden level, with 7 to 11 nodes, and an output level consisting of a single node set to produce a binary root/not-root output. The inputs to the model consisted of two linear arrays, one each from a horizontal and a vertical derivative image produced from the raw image by the Savitzky-Golay algorithm. A training image was constructed by hand-editing the raw image. The back propagation model was trained by repeatedly presenting it with a set of inputs and an associated target response. The error was calculated for the output from each input/target response pair and corrections to the weighting functions were made using a gradient descent back correction algorithm. The results of this study suggest that the ANS approach has potential to identify roots in images of soil. Suggestions for improving the performance of the model are presented. |
| Author | Nater, Edward A. Nater, Keith D. Baker, John M. |
| Author_xml | – sequence: 1 givenname: Edward A. surname: Nater fullname: Nater, Edward A. organization: Soil Science Department, University of Minnesota, St. Paul, MN 55108, USA – sequence: 2 givenname: Keith D. surname: Nater fullname: Nater, Keith D. organization: Science Applications International Corporation, San Diego, CA 92121, USA – sequence: 3 givenname: John M. surname: Baker fullname: Baker, John M. organization: USDA-ARS, Soil Science Department, University of Minnesota, St. Paul, MN 55108, USA |
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| CitedBy_id | crossref_primary_10_1007_s00138_008_0179_2 crossref_primary_10_1007_s11104_008_9651_z crossref_primary_10_1590_S0100_41582002000500013 crossref_primary_10_1111_j_1469_8137_2007_02271_x crossref_primary_10_1007_s11104_019_04132_8 crossref_primary_10_1007_s00138_006_0024_4 crossref_primary_10_1007_BF02257535 |
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