ISBN:
9783036597980
,
9783036597997
Language:
Undetermined
Pages:
1 Online-Ressource (310 p.)
Keywords:
Technology: general issues
;
History of engineering & technology
;
soil moisture content
;
spectral processing technology
;
hyperspectral
;
principal component analysis
;
feature parameters extraction
;
yield estimation
;
rice
;
unmanned aerial vehicle (UAV)
;
tasseled cap transformation
;
precision agriculture
;
weed identification
;
YOLOv4-Tiny
;
attention mechanism
;
multiscale detection
;
angle normalization
;
vegetation canopy reflectance
;
geostationary satellite
;
path length correction
;
Minnaert model
;
GOCI
;
winter wheat
;
LSTM
;
LAI
;
deep learning
;
land use
;
land cover
;
classification
;
random forest
;
Sentinel data
;
SRTM
;
feature selection
;
accuracy
;
validation
;
unmanned aerial vehicle
;
soybean
;
convolutional neural network
;
multispectral imagery
;
fusarium head blight
;
texture indices
;
machine learning
;
cropland
;
multi-seasonal
;
fractal feature
;
feature extraction
;
accuracy evaluation
;
black soil
;
UAV
;
chlorophyll
;
fractional vegetation cover
;
maturity monitoring
;
anomaly detection
;
smart agriculture
;
detection of apple leaf diseases
;
YOLOv5
;
transformer
;
CBAM
;
crop type classification
;
multi-temporal
;
remote sensing
;
dairy cows
;
body condition score
;
3D TOF sensor
;
non-contact evaluation
;
recognize area of interest
;
sugarcane clones
;
canopy cover
;
light interception
;
biomass
;
cane yield
;
peanut southern blight
;
reflection spectrum
;
spectral index
;
continuous wavelet transform
;
VGNet
;
corn diseases
;
leaf detection
;
lightweight
;
transfer learning
;
agriculture
;
n/a
Abstract:
Agricultural production management is facing a new era of intelligence and automation. With developments in sensor technologies, the temporal, spectral, and spatial resolution from ground/air/space platforms have been notably improved. Optical sensors play an essential role in agriculture production management. Specifically, monitoring plant health, growth conditions, and insect infestation has traditionally involved extensive fieldwork. We believe that sensors, artificial intelligence, and machine learning are not simply scientific experiments but opportunities to make our agricultural production management more efficient and cost-effective, further contributing to the healthy development of natural–human systems. This reprint compiles the latest research on optical sensors and machine learning in agricultural monitoring, including related topics: Machine learning approaches for crop health, growth, and yield monitoring; Combined multisource/multi-sensor data to improve the crop parameters mapping; Crop-related growth models, artificial intelligence models, algorithms, and precision management; Farmland environmental monitoring and management; Ground, air, and space platforms application in precision agriculture; Development and application of field robotics; High-throughput field information survey; Phenological monitoring
Note:
English
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