Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision
Humidity is one of significant factors affecting the quantity of Pleurotus eryngii primordium. Artificial statistics are necessary to count the number of primordium, since the model for prediction of the formation rate of primordium has not been developed. In this paper, computer vision based on sta...
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| Language: | English |
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Zhejiang University Press
2017-03-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2016.04.113 |
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| author | ZHOU Jun DING Wenjie ZHU Xuejun CAO Junyi NIU Xueming |
| author_facet | ZHOU Jun DING Wenjie ZHU Xuejun CAO Junyi NIU Xueming |
| author_sort | ZHOU Jun |
| collection | DOAJ |
| description | Humidity is one of significant factors affecting the quantity of Pleurotus eryngii primordium. Artificial statistics are necessary to count the number of primordium, since the model for prediction of the formation rate of primordium has not been developed. In this paper, computer vision based on statistics was applied to develop a formation rate model for primordium. To solve the problem of statistics on primordium, image preprocessing and gray recognition template extraction were firstly studied. The number of primordium was accounted on the basis of primordium size. However, recognition rate was low because of the similarity between primordium and background. Second, combined with the gray image matrix of primordium, a characteristic-genetic-screening method based on size and shape of primordium was proposed to extract the morphological characteristics of primordium seed, and a feature library of primordium seeds was built to display the characteristic data information. Then, the large data analysis was carried out on the morphological database based on genetic idea, and 12 seeds were acquired. A primordium quantity neural network prediction model was established based on back-propagation neural network in which matching quantity of primordium seeds was considered as input, the actual quantity of primordium as output. Primordium statistics were completed and verified, with accuracy up to 94.79%. According to the statistics on the primordium under different relative humidity conditions, the formation rate model of primordium was established. It is found that computer vision based statistical method for primordium can be used to evaluate the formation rate of primordium under different humidity conditions. |
| format | Article |
| id | doaj-art-da2a5754599b4a7ab8ad9d88c19aa307 |
| institution | Kabale University |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2017-03-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-da2a5754599b4a7ab8ad9d88c19aa3072025-08-20T03:58:18ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552017-03-014326227210.3785/j.issn.1008-9209.2016.04.11310089209Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer visionZHOU JunDING WenjieZHU XuejunCAO JunyiNIU XuemingHumidity is one of significant factors affecting the quantity of Pleurotus eryngii primordium. Artificial statistics are necessary to count the number of primordium, since the model for prediction of the formation rate of primordium has not been developed. In this paper, computer vision based on statistics was applied to develop a formation rate model for primordium. To solve the problem of statistics on primordium, image preprocessing and gray recognition template extraction were firstly studied. The number of primordium was accounted on the basis of primordium size. However, recognition rate was low because of the similarity between primordium and background. Second, combined with the gray image matrix of primordium, a characteristic-genetic-screening method based on size and shape of primordium was proposed to extract the morphological characteristics of primordium seed, and a feature library of primordium seeds was built to display the characteristic data information. Then, the large data analysis was carried out on the morphological database based on genetic idea, and 12 seeds were acquired. A primordium quantity neural network prediction model was established based on back-propagation neural network in which matching quantity of primordium seeds was considered as input, the actual quantity of primordium as output. Primordium statistics were completed and verified, with accuracy up to 94.79%. According to the statistics on the primordium under different relative humidity conditions, the formation rate model of primordium was established. It is found that computer vision based statistical method for primordium can be used to evaluate the formation rate of primordium under different humidity conditions.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2016.04.113<italic>Pleurotus eryngii</italic> primordiumgrayscale imageimage recognitionseed |
| spellingShingle | ZHOU Jun DING Wenjie ZHU Xuejun CAO Junyi NIU Xueming Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision 浙江大学学报. 农业与生命科学版 <italic>Pleurotus eryngii</italic> primordium grayscale image image recognition seed |
| title | Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision |
| title_full | Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision |
| title_fullStr | Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision |
| title_full_unstemmed | Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision |
| title_short | Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision |
| title_sort | evaluation on formation rate of pleurotus eryngii primordium under different humidity conditions by computer vision |
| topic | <italic>Pleurotus eryngii</italic> primordium grayscale image image recognition seed |
| url | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2016.04.113 |
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