Crop Stress Sensing and Plant Phenotyping Systems: A Review

doi:10.12133/j.smartag.SA202211001

Abstract

Abstract:

Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner.

Key words: crop stress sensing, plant phenotyping, variable-rate field management, HTTP system

CLC Number:

BAI Geng, GE Yufeng. Crop Stress Sensing and Plant Phenotyping Systems: A Review[J]. Smart Agriculture, 2023, 5(1): 66-81.

Figures/Tables 10

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References 95

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Method	Name	Instrumentation	Extracted parameters	References
I1	Feel and appearance	None	Grower's observation	［39］
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	［36］
I3	Soil water potential	Granular matrix sensors	Soil water potential	［32］
I4	Crop water stress index （CWSI）	1. Infrared thermometer 2. Weather station	1. Canopy temperature 2. Weather data	［33， 34］
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	［35， 40］
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	［37， 41-43］
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	［21， 38， 44， 45］

Method	Name	Instrumentation	Extracted parameters	References
N1	Producer experience	Leaf color chart	Subjective leaf color	［52］
N2	Chlorophyll content	SPAD chlorophyll meter	Leaf chlorophyll content	［53］
N3	Lab analysis	Nitrogen analyzer	Leaf nitrogen concentration	［20］
N4	Leaf spectral scan	VIS-NIR-SWIR spectrometer	Leaf spectral reflectance	［20］
N5	Active-optical reflectance sensor （AORS） algorithm	Active two-band NDVI sensor	NDVI-related vegetation indices	［55， 56， 60］
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.	［57］
N7	Aerial platforms	Hyperspectral camera	Vegetation indices or reflectance spectrum	［58， 59， 61］

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	［80， 86］
B5	Disease and insects	Deep learning—based diagnose	RGB camera， Hyperspectral camera	Deep learning	［81-83， 87］
B6	General crop status	High-resolution satellite network	Multispectral camera	Traditional and deep learning	［23， 88］
B7	General crop status	Ag IoT network	Various types of sensors	Traditional and deep learning	［85， 89］

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

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