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Table 1 An analytical study on examples of crop categorisation approaches; demonstrates: the type of categorisation application, the used learning algorithm, the data type, data pre-processing and selected features for each algorithm

From: Data analytics for crop management: a big data view

References

Application

Algo

Target

Data type

Data pre-processing

Extracted features

[31]

Crop fieldsmapping

RF

/

SatelliteDigitalGlobe Worldview-2

Hand digitisation

Randomised Quasi-Exhaustive features

[41]

Crop mapping

Decision tree

Soybean

Satellite

Multi resolutionsegmentation

NDVI NIR (near infrared) SWIR (short wave infrared)

[131]

Crops mapping

RF

/

GF-1 WFV sensorsatellite images

Multi-resolutionsegmentation

temporal, spectral textural features vegetation indexes(NDVI, EVI...)

[22]

Crop fieldsmapping

RF

Paddy rice

Satellite images

Polarisation for cloud contamination by Google Earth Engine

NDVI, EVIland surface water index LSWI

[115]

Crop mapping

Deep learning: autoencoder CNN, Full CNN

Soybean, maize cotton

Satellite images

Data were pre-processed

Texturepixel’s features based on the image patch

[157]

Crop classification

LSTM

/

Satellite & opticalimagesfield surveys

Segmentaionpan-sharpening and mosaic of optical imagesthermal noise removal and radiometric correction

Spatial features

[76]

Crop classification

Deep learning CNN

Wheat, maize sunflower soybeans, sugar beet

Satellite images

segmentation and data restoration using unsupervised NN self-organising Kohonen maps)

Spectral and spatial features

[33]

Plant classification

Deep learningCNN

22 plants

Camera and cell phone images

Data are not pre-processed

Self-learned features

[26]

Crop classification

Ensemble learningANN, DT, SVM

Rice, soybean, corn cotton

Remote sensing images

USGS online system, used a cubic convolution 245 re-sampling and a standard terrain correction incorporating ground truth points

NDVI, levels of greenmoisture

[100]

Crop classification

set of classifiers SVM(RBF kernel), RF, Spectral Angle Mapper

Tree crops, sugar beet alfalfa, cereals

Sensor satellite Time series and images

Atmospheric correction and Radiometric calibration and Pan-Sharpening

NDVI