Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing
Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/945 |
| _version_ | 1797476837353324544 |
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| author | Fengnong Chen Yao Zhang Jingcheng Zhang Lianmeng Liu Kaihua Wu |
| author_facet | Fengnong Chen Yao Zhang Jingcheng Zhang Lianmeng Liu Kaihua Wu |
| author_sort | Fengnong Chen |
| collection | DOAJ |
| description | Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and <i>U. virens</i> infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work. |
| first_indexed | 2024-03-09T21:08:24Z |
| format | Article |
| id | doaj.art-a9f2c36a66b64186a102ee24fe3e29bd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:24Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a9f2c36a66b64186a102ee24fe3e29bd2023-11-23T21:54:37ZengMDPI AGRemote Sensing2072-42922022-02-0114494510.3390/rs14040945Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote SensingFengnong Chen0Yao Zhang1Jingcheng Zhang2Lianmeng Liu3Kaihua Wu4College of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaChina National Rice Research Institute, Hangzhou 310006, ChinaCollege of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaRice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and <i>U. virens</i> infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work.https://www.mdpi.com/2072-4292/14/4/945rice false smut (<i>Ustilaginoidea virens</i>)complex plant environmentmixed discriminant methodsnear earth remote sensingdetectionprescription map |
| spellingShingle | Fengnong Chen Yao Zhang Jingcheng Zhang Lianmeng Liu Kaihua Wu Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing Remote Sensing rice false smut (<i>Ustilaginoidea virens</i>) complex plant environment mixed discriminant methods near earth remote sensing detection prescription map |
| title | Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing |
| title_full | Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing |
| title_fullStr | Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing |
| title_full_unstemmed | Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing |
| title_short | Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing |
| title_sort | rice false smut detection and prescription map generation in a complex planting environment with mixed methods based on near earth remote sensing |
| topic | rice false smut (<i>Ustilaginoidea virens</i>) complex plant environment mixed discriminant methods near earth remote sensing detection prescription map |
| url | https://www.mdpi.com/2072-4292/14/4/945 |
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