Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation
Special symptoms could be observed on rice leaves when exposed to potassium deficiency, and these symptoms usually display differently under different potassium levels, which offer a foundation for rapid nutrition diagnosis. In this research study, two years of hydroponic experiments on rice (provid...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2019/4623545 |
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| _version_ | 1832545414293749760 |
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| author | Lisu Chen Shihan Huang Yuanyuan Sun Enyan Zhu Ke Wang |
| author_facet | Lisu Chen Shihan Huang Yuanyuan Sun Enyan Zhu Ke Wang |
| author_sort | Lisu Chen |
| collection | DOAJ |
| description | Special symptoms could be observed on rice leaves when exposed to potassium deficiency, and these symptoms usually display differently under different potassium levels, which offer a foundation for rapid nutrition diagnosis. In this research study, two years of hydroponic experiments on rice (providing 5 levels of potassium nutrition from extremely short to normal) were carried out and the leaf images were acquired by optical scanning at four growth periods. To diagnose the potassium nutrition content, the special symptoms including the yellowish brown leaf margin and the necrotic spots were segmented and quantized by the object-oriented method from leaf images, and the 6 further spectral characteristics of leaf were extracted by the image color analyzing function of MATLAB software. Based on the relationship between potassium content and leaf characteristics, the G value (average value of G channel in the RGB color model) calculated from the entire leaf and leaf tip, the area of yellowish leaf margin, and the number of necrotic spots were applied in the establishment of the identification model of potassium stress by using the support vector machine (SVM). The results indicated that the overall identification accuracies of rice potassium nutrition contents were 90%, 94%, 94%, and 96% at four different growth periods (productive tillering stage, invalid tillering stage, jointing stage, and booting stage), respectively. The data obtained from another year were used to validate the model, and the identification accuracies were 94%, 78%, 80%, and 84%, respectively. Generally speaking, the extraction of the specific symptoms by using object-oriented segmentation is an extension of machine vision technology in diagnosing potassium deficiency, and its application in diagnosing plant nutrition is valuable for the quantization of effective characteristics and improvement of identification accuracy. |
| format | Article |
| id | doaj-art-ff566277b35f4b08b1f031398d882bbe |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-ff566277b35f4b08b1f031398d882bbe2025-02-03T07:25:55ZengWileyJournal of Spectroscopy2314-49202314-49392019-01-01201910.1155/2019/46235454623545Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented SegmentationLisu Chen0Shihan Huang1Yuanyuan Sun2Enyan Zhu3Ke Wang4College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaCollege of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaSchool of Data Science, Qingdao University of Science and Technology, Qingdao, Shandong, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, ChinaSpecial symptoms could be observed on rice leaves when exposed to potassium deficiency, and these symptoms usually display differently under different potassium levels, which offer a foundation for rapid nutrition diagnosis. In this research study, two years of hydroponic experiments on rice (providing 5 levels of potassium nutrition from extremely short to normal) were carried out and the leaf images were acquired by optical scanning at four growth periods. To diagnose the potassium nutrition content, the special symptoms including the yellowish brown leaf margin and the necrotic spots were segmented and quantized by the object-oriented method from leaf images, and the 6 further spectral characteristics of leaf were extracted by the image color analyzing function of MATLAB software. Based on the relationship between potassium content and leaf characteristics, the G value (average value of G channel in the RGB color model) calculated from the entire leaf and leaf tip, the area of yellowish leaf margin, and the number of necrotic spots were applied in the establishment of the identification model of potassium stress by using the support vector machine (SVM). The results indicated that the overall identification accuracies of rice potassium nutrition contents were 90%, 94%, 94%, and 96% at four different growth periods (productive tillering stage, invalid tillering stage, jointing stage, and booting stage), respectively. The data obtained from another year were used to validate the model, and the identification accuracies were 94%, 78%, 80%, and 84%, respectively. Generally speaking, the extraction of the specific symptoms by using object-oriented segmentation is an extension of machine vision technology in diagnosing potassium deficiency, and its application in diagnosing plant nutrition is valuable for the quantization of effective characteristics and improvement of identification accuracy.http://dx.doi.org/10.1155/2019/4623545 |
| spellingShingle | Lisu Chen Shihan Huang Yuanyuan Sun Enyan Zhu Ke Wang Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation Journal of Spectroscopy |
| title | Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation |
| title_full | Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation |
| title_fullStr | Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation |
| title_full_unstemmed | Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation |
| title_short | Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation |
| title_sort | rapid identification of potassium nutrition stress in rice based on machine vision and object oriented segmentation |
| url | http://dx.doi.org/10.1155/2019/4623545 |
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