Smart Agriculture ›› 2023, Vol. 5 ›› Issue (1): 66-81.doi: 10.12133/j.smartag.SA202211001
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Received:
2022-11-07
Online:
2023-03-30
corresponding author:
BAI Geng, Ph.D, research assistant professor, research interest: plant phenotyping, precision field management, and crop models. E-mail: gbai2@unl.eduCLC Number:
BAI Geng, GE Yufeng. Crop Stress Sensing and Plant Phenotyping Systems: A Review[J]. Smart Agriculture, 2023, 5(1): 66-81.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202211001
Table 1
Sensing methods for irrigation scheduling using variable-rate irrigation (VRI) systems
Method | Name | Instrumentation | Extracted parameters | References |
---|---|---|---|---|
I1 | Feel and appearance | None | Grower's observation | [ |
I2 | Soil water balance | 1. Neutron probe 2. Time Domain reflectometry 3. Capacitance probes 4. Tensiometers 5. Granular matrix sensors | 1. Allowable soil water depletion 2. Soil water depletion | [ |
I3 | Soil water potential | Granular matrix sensors | Soil water potential | [ |
I4 | Crop water stress index (CWSI) | 1. Infrared thermometer 2. Weather station | 1. Canopy temperature 2. Weather data | [ |
I5 | Soil water balance with reference evapotranspiration (ET) | 1. Soil water sensors 2. Weather station | 1. Volumetric water content at root zones 2. Reference ET 3. Crop coefficient | [ |
I6 | Integrated CWSI | 1. A wireless sensor network of Infrared thermometers 2. Weather station | 1. Canopy temperature 2. Weather data 3. Volumetric water content at root zones | [ |
I7 | Soil water balance with canopy reflectance | 1. Satellite imagery 2. Aerial imagery 3. Weather station 4. Neutron probe | 1. Volumetric water content at root zones 2. Reference ET 3. Crop coefficient | [ |
Table 2
Sensing methods for variable-rate application of synthetic nitrogen fertilizer
Method | Name | Instrumentation | Extracted parameters | References |
---|---|---|---|---|
N1 | Producer experience | Leaf color chart | Subjective leaf color | [ |
N2 | Chlorophyll content | SPAD chlorophyll meter | Leaf chlorophyll content | [ |
N3 | Lab analysis | Nitrogen analyzer | Leaf nitrogen concentration | [ |
N4 | Leaf spectral scan | VIS-NIR-SWIR spectrometer | Leaf spectral reflectance | [ |
N5 | Active-optical reflectance sensor (AORS) algorithm | Active two-band NDVI sensor | NDVI-related vegetation indices | [ |
N6 | Extended AORS | 1. Active three-band NDVI sensor 2. Electrical conductivity sensor 3. Weather data | 1. NDVI-related vegetation indices 2. Soil texture, apparent electrical conductivity, bulk density, moisture, etc. 3. Growing degree day, precipitation, etc. | [ |
N7 | Aerial platforms | Hyperspectral camera | Vegetation indices or reflectance spectrum | [ |
Table 3
A case summary in biotic stress detection for crop production
Method | Target stress | Name | Sensing instruments | Algorithm | References |
---|---|---|---|---|---|
B1 | Weeds | John Deer's See & Spray | RGB camera | Deep learning | Vendor website |
B2 | Weeds | Trimble Agriculture's Weedseeker 2 | Active NDVI sensor | NDVI threshold | Vendor website |
B3 | Weeds | Small Robot Company's Tom and Dick | RGB camera | Deep learning | Vendor website |
B4 | Disease and insects | Traditional image processing | RGB, multispectral,hyperspectral, thermal cameras | Traditional and deep learning | [ |
B5 | Disease and insects | Deep learning—based diagnose | RGB camera, Hyperspectral camera | Deep learning | [ |
B6 | General crop status | High-resolution satellite network | Multispectral camera | Traditional and deep learning | [ |
B7 | General crop status | Ag IoT network | Various types of sensors | Traditional and deep learning | [ |
Table 4
Instrumentation of the greenhouse phenotyping cart
Camera type | Camera info | Other Parameters |
---|---|---|
Thermal camera | SC640, FLIR, OR, USA | 640×480 pixels Canopy temperature |
NIR monochrome camera | DCC3240N, Thorlabs, NJ, USA | 1280×1024 pixels Canopy coverage |
6-band filter wheel | FW102C, Thorlabs, NJ, USA | 6 bands: 530, 570, 670, 770, 870, and 970 nm Canopy coverage and NDVI reflectance |
Table 5
Instrumentation of Phenocart and corresponding phenotypic parameters
Sensor type | Sensor info | Other Parameters |
---|---|---|
RGB camera | C615, Logitech, Lausanne, Switzerland | 1920×1080 pixels Canopy coverage |
Ultrasonic sensor | ToughSonic30, Senix Corporation, VT, USA. | Canopy height |
Thermal radiometer | SI-131, Apogee Instruments, Logan, UT, USA. | Plot temperature |
NDVI sensor | SRS, Meter Group, WA, USA. | Plot NDVI |
Spectrometer | CCS175, Thorlabs, NJ, USA | Plot reflectance |
Air temperature and relative humidity sensor | HMP45C-L, Campbell Sci., UT, USA | Air temperature and relative humidity |
GPS | AgGPS 162, Trimble Agriculture, CA, USA | Location and Timestamp |
Table 6
Instrumentation of NU-Spidercam HTPP facility
Sensor type | Sensor info | Other Parameters |
---|---|---|
Multispectral camera | AD080GE, JAI, Miyazaki, Japan | 1024×768 pixel Canopy coverage |
Thermal camera | A655sc, Teledyne FLIR, OR, USA | 640×480 pixels Canopy and soil temperature |
Spectrometer | HR2000+, Ocean Insight, FL, USA | Plot reflectance |
LiDAR | VLP-16 Puck, Velodyne, CA, USA | Canopy height and structure |
Hyperspectral camera | HSV101, Middleton, WI, USA | 362-1043 nm Canopy reflectance |
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