Predicting the Spread of Forest Diseases and Pests
Due to severe economic losses caused by forest diseases and pests in China, prediction for the spread of forest diseases and pests has become one of the most challenging and hottest issues. The most previous solutions have at least the following three disadvantages: (1) lacking effective utilization...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9247194/ |
| _version_ | 1818734829808648192 |
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| author | Zhihe Zhao Meng Yang Liuming Yang Qi Yuan Xiaoyu Chi Wenping Liu |
| author_facet | Zhihe Zhao Meng Yang Liuming Yang Qi Yuan Xiaoyu Chi Wenping Liu |
| author_sort | Zhihe Zhao |
| collection | DOAJ |
| description | Due to severe economic losses caused by forest diseases and pests in China, prediction for the spread of forest diseases and pests has become one of the most challenging and hottest issues. The most previous solutions have at least the following three disadvantages: (1) lacking effective utilization of image data; (2) only supporting one-dimensional prediction value, which provides limited information; (3) limiting to a small scale (e.g., sample-plot), rather than a large scale like a forest zone. Therefore, we propose an algorithm for the spread prediction based on linear regression applied to a large regional spread of forest diseases and pests. Compared to the most conventional numerical prediction, our prediction method works on two dimensions. Specifically, the diseases and pests areas are fitted by a group of cubic B-spline curves and a defined energy function is provided to describe the difference between the contours of the future time and the current time. Then, linear regression is applied to predict the spread parameters (the distance and angle), adhering to prior forestry research. After two-step corrections, the final predicted contour is obtained. Finally, we devise an appropriate 3D interactive visualization. Experimental results indicate that the proposed algorithm can effectively predict the spread of forest diseases and pests, providing forestry workers with visual aids of the future situation. |
| first_indexed | 2024-12-18T00:11:35Z |
| format | Article |
| id | doaj.art-070e9c06c89741d1a787293d017df56f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:11:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-070e9c06c89741d1a787293d017df56f2022-12-21T21:27:40ZengIEEEIEEE Access2169-35362020-01-01819980319981210.1109/ACCESS.2020.30355479247194Predicting the Spread of Forest Diseases and PestsZhihe Zhao0https://orcid.org/0000-0001-5398-6502Meng Yang1Liuming Yang2Qi Yuan3Xiaoyu Chi4Wenping Liu5School of Information Science and Technology, Beijing Forestry University, Beijing, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaQingdao Research Institute, Beihang University, Qingdao, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaDue to severe economic losses caused by forest diseases and pests in China, prediction for the spread of forest diseases and pests has become one of the most challenging and hottest issues. The most previous solutions have at least the following three disadvantages: (1) lacking effective utilization of image data; (2) only supporting one-dimensional prediction value, which provides limited information; (3) limiting to a small scale (e.g., sample-plot), rather than a large scale like a forest zone. Therefore, we propose an algorithm for the spread prediction based on linear regression applied to a large regional spread of forest diseases and pests. Compared to the most conventional numerical prediction, our prediction method works on two dimensions. Specifically, the diseases and pests areas are fitted by a group of cubic B-spline curves and a defined energy function is provided to describe the difference between the contours of the future time and the current time. Then, linear regression is applied to predict the spread parameters (the distance and angle), adhering to prior forestry research. After two-step corrections, the final predicted contour is obtained. Finally, we devise an appropriate 3D interactive visualization. Experimental results indicate that the proposed algorithm can effectively predict the spread of forest diseases and pests, providing forestry workers with visual aids of the future situation.https://ieeexplore.ieee.org/document/9247194/Forest diseases and pestspredictionspread |
| spellingShingle | Zhihe Zhao Meng Yang Liuming Yang Qi Yuan Xiaoyu Chi Wenping Liu Predicting the Spread of Forest Diseases and Pests IEEE Access Forest diseases and pests prediction spread |
| title | Predicting the Spread of Forest Diseases and Pests |
| title_full | Predicting the Spread of Forest Diseases and Pests |
| title_fullStr | Predicting the Spread of Forest Diseases and Pests |
| title_full_unstemmed | Predicting the Spread of Forest Diseases and Pests |
| title_short | Predicting the Spread of Forest Diseases and Pests |
| title_sort | predicting the spread of forest diseases and pests |
| topic | Forest diseases and pests prediction spread |
| url | https://ieeexplore.ieee.org/document/9247194/ |
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