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Image based soil characterization for precision agriculture
Authors: Perry Taneja,
Asim Biswas,
Prasad Daggupati
Identifier: CSBE19176
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Published in: CSBE-SCGAB Technical Conferences » AGM Vancouver 2019
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Description: A precise and accurate assessment of soil organic carbon (SOC) and soil moisture content (SMC) is critical for applications in the fields of engineering, environment, and agriculture. However, characterization and measurement of these properties is expensive, time-consuming and labor-intensive. Previous researches have shown that soils exhibits spectral re?ectance characteristics which can be related with various soil properties to provide for indirect measurement of soil properties. For example, with advances in technological and computational facilities, high resolution digital images and computer vision algorithms have demonstrated potential to provide quick and nondestructive characterization of soil properties. The objective of this study is to develop an algorithm to quantify SOC and SMC from digital images taken in the laboratory. Soil images will be collected in laboratory on a range of soils collected from various fields with diverse soil type. Several image color and texture parameters will be extracted from digital images and predictive relationships will be developed with the laboratory measured soil properties. It has also been established that dark color in soils can be due to both high SOM content as well as high SMC. This dimension will also be researched. This study will help in fabricating an efficient sensor which can be used to provide fast, accurate and nondestructive predictions of soil properties. Thus, a potential area which can be targeted for future work is making computer vision algorithms work in in-situ conditions, so that the goal of digital image processing to provide ease in analysis work can be achieved.
Keywords: digital images, computer vision, nondestructive characterization, modelling.
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Date: 2019-07-15
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Conference name: CSBE/SCGAB 2019 Annual Conference, Vancouver, BC, 14-17 July 2019.
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Type: Poster
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Publication type: Text.Abstract
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Coverage: Canada
Language 1: en
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Rights: Canadian Society for Bioengineering
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DOI:
Description: A precise and accurate assessment of soil organic carbon (SOC) and soil moisture content (SMC) is critical for applications in the fields of engineering, environment, and agriculture. However, characterization and measurement of these properties is expensive, time-consuming and labor-intensive. Previous researches have shown that soils exhibits spectral re?ectance characteristics which can be related with various soil properties to provide for indirect measurement of soil properties. For example, with advances in technological and computational facilities, high resolution digital images and computer vision algorithms have demonstrated potential to provide quick and nondestructive characterization of soil properties. The objective of this study is to develop an algorithm to quantify SOC and SMC from digital images taken in the laboratory. Soil images will be collected in laboratory on a range of soils collected from various fields with diverse soil type. Several image color and texture parameters will be extracted from digital images and predictive relationships will be developed with the laboratory measured soil properties. It has also been established that dark color in soils can be due to both high SOM content as well as high SMC. This dimension will also be researched. This study will help in fabricating an efficient sensor which can be used to provide fast, accurate and nondestructive predictions of soil properties. Thus, a potential area which can be targeted for future work is making computer vision algorithms work in in-situ conditions, so that the goal of digital image processing to provide ease in analysis work can be achieved.
Keywords: digital images, computer vision, nondestructive characterization, modelling.
Résumé:
Mots-clés:
Citation:
Volume:
Issue:
Pages -
Contributor:
Date: 2019-07-15
Technical field:
Conference name: CSBE/SCGAB 2019 Annual Conference, Vancouver, BC, 14-17 July 2019.
Session name:
Other information:
Type: Poster
Format:
Publication type: Text.Abstract
Source:
Relation:
Coverage: Canada
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
Notes:
Other files:
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