Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves
Facilitating non-contact measurement, a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD) camera paired with two optical filters and light chamber was used to acquire green (550 ± 40 nm) and red band (700 ± 40 nm) ima...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-12-01
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| Series: | Information Processing in Agriculture |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221431731730080X |
| _version_ | 1797700726008315904 |
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| author | M.S. Borhan S. Panigrahi M.A. Satter H. Gu |
| author_facet | M.S. Borhan S. Panigrahi M.A. Satter H. Gu |
| author_sort | M.S. Borhan |
| collection | DOAJ |
| description | Facilitating non-contact measurement, a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD) camera paired with two optical filters and light chamber was used to acquire green (550 ± 40 nm) and red band (700 ± 40 nm) images from the same leaf. Potato leaves from 15 plants differing in coloration (green to yellow) and age were selected for this study. Histogram based image features, such as mean and variances of green and red band images, were extracted from the histogram. Regression analyses demonstrated that the variations in SPAD meter reading could be explained by the mean gray and variances of gray scale values. The fitted least square models based on the mean gray scale levels were inversely related to the chlorophyll content of the potato leaf with a R2 of 0.87 using a green band image and with an R2 of 0.79 using a red band image. With the extracted four image features, the developed multiple linear regression model predicted the chlorophyll content with a high R2 of 0.88). The multiple regression model (using all features) provided an average prediction accuracy of 85.08% and a maximum accuracy of 99.8%. The prediction model using only mean gray value of red band showed an average accuracy of 81.6% with a maximum accuracy of 99.14%. Keywords: Computer imaging, Chlorophyll, SPAD meter, Regression, Prediction accuracy |
| first_indexed | 2024-03-12T04:25:48Z |
| format | Article |
| id | doaj.art-4adf4500563e41d3ace1a06bb8192cd0 |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T04:25:48Z |
| publishDate | 2017-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-4adf4500563e41d3ace1a06bb8192cd02023-09-03T10:23:13ZengElsevierInformation Processing in Agriculture2214-31732017-12-0144275282Evaluation of computer imaging technique for predicting the SPAD readings in potato leavesM.S. Borhan0S. Panigrahi1M.A. Satter2H. Gu3Research Specialist, Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA; Corresponding author at: Agricultural and Biosystems Engineering, North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA.Professor of Electrical and Computer Engineering Technology, Purdue University West Lafayette, IN 47907, USA; and former professor of Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USAGraduate Student, Computer Science Department, North Dakota State University, Fargo, ND 58105, USASenior Software Developer, Psychology Department, North Dakota State University, Fargo, ND 58105, USAFacilitating non-contact measurement, a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD) camera paired with two optical filters and light chamber was used to acquire green (550 ± 40 nm) and red band (700 ± 40 nm) images from the same leaf. Potato leaves from 15 plants differing in coloration (green to yellow) and age were selected for this study. Histogram based image features, such as mean and variances of green and red band images, were extracted from the histogram. Regression analyses demonstrated that the variations in SPAD meter reading could be explained by the mean gray and variances of gray scale values. The fitted least square models based on the mean gray scale levels were inversely related to the chlorophyll content of the potato leaf with a R2 of 0.87 using a green band image and with an R2 of 0.79 using a red band image. With the extracted four image features, the developed multiple linear regression model predicted the chlorophyll content with a high R2 of 0.88). The multiple regression model (using all features) provided an average prediction accuracy of 85.08% and a maximum accuracy of 99.8%. The prediction model using only mean gray value of red band showed an average accuracy of 81.6% with a maximum accuracy of 99.14%. Keywords: Computer imaging, Chlorophyll, SPAD meter, Regression, Prediction accuracyhttp://www.sciencedirect.com/science/article/pii/S221431731730080X |
| spellingShingle | M.S. Borhan S. Panigrahi M.A. Satter H. Gu Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves Information Processing in Agriculture |
| title | Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves |
| title_full | Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves |
| title_fullStr | Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves |
| title_full_unstemmed | Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves |
| title_short | Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves |
| title_sort | evaluation of computer imaging technique for predicting the spad readings in potato leaves |
| url | http://www.sciencedirect.com/science/article/pii/S221431731730080X |
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