Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor
Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geome...
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| Format: | Article |
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MDPI AG
2018-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/8/2711 |
| _version_ | 1798024392934948864 |
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| author | Peikui Huang Xiwen Luo Jian Jin Liangju Wang Libo Zhang Jie Liu Zhigang Zhang |
| author_facet | Peikui Huang Xiwen Luo Jian Jin Liangju Wang Libo Zhang Jie Liu Zhigang Zhang |
| author_sort | Peikui Huang |
| collection | DOAJ |
| description | Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping. |
| first_indexed | 2024-04-11T18:01:40Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:01:40Z |
| publishDate | 2018-08-01 |
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| series | Sensors |
| spelling | doaj.art-3623c6387c804765adc181ac0ea5897f2022-12-22T04:10:26ZengMDPI AGSensors1424-82202018-08-01188271110.3390/s18082711s18082711Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth SensorPeikui Huang0Xiwen Luo1Jian Jin2Liangju Wang3Libo Zhang4Jie Liu5Zhigang Zhang6Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaDepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USADepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USADepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USACollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaHyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping.http://www.mdpi.com/1424-8220/18/8/2711high-throughput phenotypingclose-range hyperspectral cameralow-cost depth sensorfusionplant 3D model |
| spellingShingle | Peikui Huang Xiwen Luo Jian Jin Liangju Wang Libo Zhang Jie Liu Zhigang Zhang Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor Sensors high-throughput phenotyping close-range hyperspectral camera low-cost depth sensor fusion plant 3D model |
| title | Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor |
| title_full | Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor |
| title_fullStr | Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor |
| title_full_unstemmed | Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor |
| title_short | Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor |
| title_sort | improving high throughput phenotyping using fusion of close range hyperspectral camera and low cost depth sensor |
| topic | high-throughput phenotyping close-range hyperspectral camera low-cost depth sensor fusion plant 3D model |
| url | http://www.mdpi.com/1424-8220/18/8/2711 |
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