Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques
Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fall...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/287 |
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| author | Sadia Sabrin Nodi Manoranjan Paul Nathan Robinson Liang Wang Sabih ur Rehman Muhammad Ashad Kabir |
| author_facet | Sadia Sabrin Nodi Manoranjan Paul Nathan Robinson Liang Wang Sabih ur Rehman Muhammad Ashad Kabir |
| author_sort | Sadia Sabrin Nodi |
| collection | DOAJ |
| description | Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user’s perception. As smartphones are widely used and come with high-quality cameras, a popular one was used for capturing images for this study. This study aims to predict Munsell soil colour (MSC) from the Munsell soil colour book (MSCB) by using deep learning techniques on mobile-captured images. MSCB contains 14 pages and 443 colour chips. So, the number of classes for chip-by-chip prediction is very high, and the captured images are inadequate to train and validate using deep learning methods; thus, a patch-based mechanism was proposed to enrich the dataset. So, the course of action is to find the prediction accuracy of MSC for both page level and chip level by evaluating multiple deep learning methods combined with a patch-based mechanism. The analysis also provides knowledge about the best deep learning technique for MSC prediction. Without patching, the accuracy for chip-level prediction is below <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>40</mn><mo>%</mo></mrow></semantics></math></inline-formula>, the page-level prediction is below <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>65</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the accuracy with patching is around <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula> for both, which is significant. Lastly, this study provides insights into the application of the proposed techniques and analysis within real-world soil and provides results with higher accuracy with a limited number of soil samples, indicating the proposed method’s potential scalability and effectiveness with larger datasets. |
| format | Article |
| id | doaj-art-bf5d35a313e146e0948fca12604cfd34 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bf5d35a313e146e0948fca12604cfd342025-01-10T13:21:28ZengMDPI AGSensors1424-82202025-01-0125128710.3390/s25010287Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning TechniquesSadia Sabrin Nodi0Manoranjan Paul1Nathan Robinson2Liang Wang3Sabih ur Rehman4Muhammad Ashad Kabir5School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, AustraliaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, AustraliaCooperative Research Centre for High Performance Soils, Callaghan, NSW 2308, AustraliaCooperative Research Centre for High Performance Soils, Callaghan, NSW 2308, AustraliaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, AustraliaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, AustraliaSoil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user’s perception. As smartphones are widely used and come with high-quality cameras, a popular one was used for capturing images for this study. This study aims to predict Munsell soil colour (MSC) from the Munsell soil colour book (MSCB) by using deep learning techniques on mobile-captured images. MSCB contains 14 pages and 443 colour chips. So, the number of classes for chip-by-chip prediction is very high, and the captured images are inadequate to train and validate using deep learning methods; thus, a patch-based mechanism was proposed to enrich the dataset. So, the course of action is to find the prediction accuracy of MSC for both page level and chip level by evaluating multiple deep learning methods combined with a patch-based mechanism. The analysis also provides knowledge about the best deep learning technique for MSC prediction. Without patching, the accuracy for chip-level prediction is below <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>40</mn><mo>%</mo></mrow></semantics></math></inline-formula>, the page-level prediction is below <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>65</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the accuracy with patching is around <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula> for both, which is significant. Lastly, this study provides insights into the application of the proposed techniques and analysis within real-world soil and provides results with higher accuracy with a limited number of soil samples, indicating the proposed method’s potential scalability and effectiveness with larger datasets.https://www.mdpi.com/1424-8220/25/1/287agriculturecomputer visionmobile phoneaugmentation |
| spellingShingle | Sadia Sabrin Nodi Manoranjan Paul Nathan Robinson Liang Wang Sabih ur Rehman Muhammad Ashad Kabir Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques Sensors agriculture computer vision mobile phone augmentation |
| title | Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques |
| title_full | Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques |
| title_fullStr | Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques |
| title_full_unstemmed | Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques |
| title_short | Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques |
| title_sort | munsell soil colour prediction from the soil and soil colour book using patching method and deep learning techniques |
| topic | agriculture computer vision mobile phone augmentation |
| url | https://www.mdpi.com/1424-8220/25/1/287 |
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