Table 2 Summary of the most common imaging sensors used in plant phenotyping experiments under different application environments
Imaging techniques | Sensor | Phenotype parameters | Imaging environment | Advantage | Limitations | Potential application |
|---|---|---|---|---|---|---|
Visible light imaging | CCD, CMOS | Tree height, tree stem, DBH, LAI, LAD, leaf greenness, leaf color, canopy volume, chlorophyll content, phenology, canopy coverage, plant diseases and pests, canopy structure, leaf distribution, leaf angle, photosynthetic status | Growth chamber; greenhouse; field | Low cost; high resolution; suitable for UAV, providing color information | Cumbersome postprocessing and limit automatically processing image; limited to visual three spectral bands; sunlight and shadows can result in under or overexposure; only provides plant physiological information, no spectral calibration; only relative measurement | Growth monitoring, phenology monitoring, pest and disease detecting, stress response, morphological structure capture |
Stereo camera, a time-of-flight camera | Tree height, tree stem, DBH, LAI, LAD, leaf greenness, leaf color, canopy volume, canopy coverage, canopy structure, leaf distribution, leaf angle | Growth chamber; greenhouse; field | High resolution; providing depth images; rapid acquisition | Difficult postprocessing; experimental conditions influence its performance; low resolution, high noise; many restrictions on taking photos; field application is limited | Growth monitoring, structure capture | |
Multispectral imaging | Multispectral camera | Canopy coverage, canopy volume, canopy structure, chlorophyll content, leaf greenness, leaf color, plant diseases and pests, photosynthetic status, water content, lignin | Growth chamber; greenhouse; field | Easy in image processing; mature technology | Limited to several spectral bands; spectral data should be frequently calibrated using referenced objects; effects of camera geometrics, illumination condition, and sun angle on the data signal | Growth monitoring, phenology monitoring, pest and disease detecting, stress response, morphological structure capture |
Hyperspectral imaging (HSI) | Hyperspectral camera | Leaf and canopy water status; leaf and canopy health status; canopy coverage, canopy volume, canopy structure, chlorophyll content, leaf greenness, leaf color, plant diseases and pests, photosynthetic rate, lignin | Growth chamber; greenhouse; field | High spectral resolution; Containing abundant spectral information with many bands; background interference can be removed; suitable for UAV | Frequent sensor calibration; low spatial resolution; cost is high; large image data sets for hyperspectral imaging; complex data interpretation; changes in ambient light conditions influence signal; canopy structure and camera geometries or sun angle influence signal; image data management is challenging | Growth monitoring, phenology monitoring, pest and disease detecting, stress response, morphological structure capture |
Thermal infrared imaging | Thermal infrared/Longwave infrared cameras (TIR/LWIR) | LAI, LAD, leaf greenness, leaf color, chlorophyll content, phenology, plant diseases and pests, canopy or leaf temperature, photosynthetic status, AGB, lignin | Growth chamber; greenhouse; field | Wide measurement range; background interference can be removed; suitable for UAV | Imaging sensor calibration and atmospheric correction are often required; changes in ambient conditions lead to changes in canopy temperature; making a comparison through time difficult; necessitating the use of reference; difficult to separate soil temperature from plant temperature in sparse canopies; limiting the automation of image processing | Growth monitoring, phenology monitoring, pest and disease detecting, stress response, temperature testing |
Fluorescence imaging (FLUO) | Fluorescence cameras and setups | Chlorophyll content, canopy coverage, plant diseases and pests, photosynthetic status, water content, lignin | Growth chamber; greenhouse; field | Sensitive to fluorescence and water stress | Difficulty in fluorescence excitation; limited field application; pre-acclimation conditions required; difficult to measure at the canopy scale because of the small signal to noise ratio | Growth monitoring, phenology monitoring, pest and disease detecting, stress response |
3D laser imagine | Laser scanning instruments | Tree height, tree stem, DBH, LAI, LAD, canopy volume, chlorophyll content, canopy structure, leaf distribution, leaf angle, AGB | Growth chamber; greenhouse; field | Long measurement distance; high precision; good penetration | High cost; wind and fog cause noise | Growth monitoring, structure capture |
Light detection and ranging (LIDAR) | LIDAR sensor | Tree height, tree stem, LAI, LAD, canopy volume, canopy volume, chlorophyll content, phenology, canopy coverage, canopy structure, leaf distribution, leaf angle, photosynthetic status, AGB | Growth chamber; greenhouse; field | Providing three-dimensional shape; suitable for UAV | High cost; sensitive to the small difference in path length; specific illumination required for some laser scanning instruments, data processing is time-consuming; integration or synchronization with GPS and encoder position systems is needed for georeferencing | Growth monitoring, structure capture |
Nuclear magnetic resonance imaging (MRI) | MRI sensor | Internal structures, metabolites, development of root systems, water presence | Growth chamber | Available for screening 3D structural information | Low throughput, data acquisition is time-consuming, software tools need to be further developed to analyze data and obtain physiologically interpretable results, and the image segmentation and reconstruction must be further improved for high throughput tree phenotyping | Acquire 3D datasets of plant structures, complete root systems growing in or near natural soil, and entire plants |