Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants
The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) plants offers important theoretical reference value in balancing the accuracy of plant identification...
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MDPI AG
2024-10-01
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| author | Xiandan Du Zhongfa Zhou Denghong Huang |
| author_facet | Xiandan Du Zhongfa Zhou Denghong Huang |
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| description | The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization. |
| format | Article |
| id | doaj-art-bc0f54ca145c4b998a4668002ebd3cae |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-10-01 |
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| series | Agriculture |
| spelling | doaj-art-bc0f54ca145c4b998a4668002ebd3cae2025-08-20T02:08:00ZengMDPI AGAgriculture2077-04722024-10-011411187110.3390/agriculture14111871Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) PlantsXiandan Du0Zhongfa Zhou1Denghong Huang2School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550025, ChinaThe exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.https://www.mdpi.com/2077-0472/14/11/1871UAV visible light imagesfrequency domain filteringOtsumorphological filteringspatial scale effectaccurate identification |
| spellingShingle | Xiandan Du Zhongfa Zhou Denghong Huang Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants Agriculture UAV visible light images frequency domain filtering Otsu morphological filtering spatial scale effect accurate identification |
| title | Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants |
| title_full | Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants |
| title_fullStr | Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants |
| title_full_unstemmed | Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants |
| title_short | Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants |
| title_sort | influence of spatial scale effect on uav remote sensing accuracy in identifying chinese cabbage i brassica rapa i subsp i pekinensis i plants |
| topic | UAV visible light images frequency domain filtering Otsu morphological filtering spatial scale effect accurate identification |
| url | https://www.mdpi.com/2077-0472/14/11/1871 |
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