Precision Viticulture Flashcards
Data collection: soil electrical conductivity sensor
measuring soil
electrical conductivity, soil
moisture, silt content, pH
Data collection: canopy sensor
estimating crop characteristics such as
vigor, biomass, Leaf Area
Index, chlorophyll content etc.
It uses spectral vegetation
indices for this purpose
Data collection: thermal camera
measuring crop water stress and pest infestation
Data collection: multispectral camera sensor
for estimating crop
characteristics using
spectral vegetation
indices
Data collection: Laser scanner sensor
for measuring crop
characteristics such as
crop height and pruning
weight
What is the IoT Four-Storage Architecture
IoT = internet of things / internet connected
TIPPY TOP:
Me interacting and analyzing data once its in the cloud
TOP:
Cloud (Analyltics, Prediction,
Integration)
-in-depth processing by utilizing Big Data techniques.
MID 2:
IoT Edge Devices
(Preprocessing with the internet, could use outside data in combination with data from below)
-Crossing the realm of IT: Data processing before entering the data center
MID 1:
Data Acquisitions Systems
(IoT Gateway) (Sensory control box)
-The collected data are being converted into digital streams.
BOTTOM:
Sensors and Actuators
-Collecting data from the environment and actuators the varying physical conditions.
NDVI
Normalized Difference Vegetation Index
sensors are used to monitor vine health, vigor, and canopy density by analyzing plant reflectance in different light wavelengths.
-difference between
Near-Infrared (NIR) and Red light reflectance
Healthy plants absorb red light (used in photosynthesis) and reflect NIR (since chlorophyll does not absorb NIR).
NDVI = (NIR - Red)/(NIR + Red)
Values range from -1 to +1:
0.6 – 1.0 → High vigor, healthy vines.
0.2 – 0.6 → Moderate vigor.
0 – 0.2 → Weak vines, stress or disease.
What are the four parts of digital viticulture?
- Data collection (soil, crop, weather, machines)
-sensors
-IoT
-GNSS
-Satillites
-Drones - Data analysis (storage, processing)
-FMIS / GIS (collection of maps)
-Geostatistics (location based stats)
-AI
-Big Data (data brokers) - Decision making (time, dosing, maps, logistics)
-DSS
-Experience
-Finance
-Regulations - Application (irrigate, spray, prune)
-humans
-machines
-site-specific applications
Application of maps
-soil texture
-topography
-elevation model
-electrical conductivity
-yield
-biomass
-imagery
Diwakopter Project
14 experimental fields in Germany to promote digitization
-drone surveys to evaluate
-drone spraying for precise application
Ground based sensor: LiDAR
Sends beams 40 times per second, it measures the time for the beam to hit the object and come back
Creates a 3D construction of the canopy.
It is used in combination with NIR (near infrared) and RGB (red, green, blue) to give a complete image of the canopy
Ground based sensor: thermal imaging
works in NIR (near infrared), by detecting temperature of the canopy
-> this is used to detect water stress before it is visible.
FMIS
farm management information system
GIS
geographical information system
How could you monitor the canopy to adjust spraying amount while driving?
Ultrasound sensors
Detect canopt density and a system can control the rate of opening the nozzels to increase or decrease spray amout
EXXACT robotics
uses LiDAR and GNSS system with 2 cm for one pixel resolution
NAIO ted robot
for weeding operations. Also uses GNSS
