Intelligent Data Mining and Fusion Systems in Agriculture

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INTELLIGENT
DATA MINING AND
FUSION SYSTEMS
IN AGRICULTURE
Copyright
1
Sensors in agriculture
	Milestones in agricultural automation and monitoring in agriculture
		Introduction
		Sensing systems for PA
		Benefits through sensing for PA farming systems
		Current trends in agricultural automation
		Current challenges of PA sensing applications
	Sensors for soil analysis and characteristics
		Visible (VIS)/NIR spectroscopy
		Soil sensing using airborne and satellite imaging
			Electrodes
			Microwaves
	Yield sensing
	Sensors for weed management
	Sensors for disease detection and classification
	References
	Further reading
2
Artificial intelligence in agriculture
	Artificial intelligence and data mining basics
	Artificial neural networks (ANNs)
		Back propagation artificial neural network
		Single-layer feedforward neural network (SLFN) and extreme learning machine (ELM)
		Radial basis function neural network
		Deep learning
			Generalization accuracy of ANNs
			Validation by using partial datasets to alleviate bias
				Hold-out
				Cross-validation
			Neural networks applicability with big data
			Indicators of dataset suitability
				Penalty-driven regularization
			Convolutional autoencoder
		Bayesian networks
		Particle swarm optimization models
		Decision tree classifier
			RF classifier
		GMM models
		Principal component analysis (PCA)
		Maximum likelihood classifier (MLC)
	Artificial neural networks applications in Biosystems engineering
	Contribution of artificial intelligence in precision agriculture (economical, practical etc.)
	One class classifiers
	Support vector machines (SVMs)
	One class-support vector machines (SVMs)
		Support vector data description (SVDD)
		One-class support-vector-learning for multi class problems
		Nearest-center strategy-nearest-support-vector strategy
	Active learning
	Querying criteria
	Online active learning
	Sampling bias
		One class classification classifiers
		SVM based one-class classifier
		Auto-encoder based one-class classifier
		MOG based one-class classifier
		Augmentation of one class classifiers
	Hierarchical self-organizing artificial neural networks
		Taxonomy of unsupervised HSOM
			Thematic agglomerative HSOM
			Agglomerative HSOM based on clusters
			Static divisive SOM
			Dynamic divisive HSOM
		HSOM implementation and applications
		Counter-propagation artificial neural networks (CP-ANNs)
		Supervised Kohonen networks (SKNs)
		XY-fused networks (XY-F)
		Supervised self-organizing map with embedded output
	References
	Further reading
3
Utilization of multisensors and data fusion in precision agriculture
	The necessity of multisensors network utilization for agriculture monitoring and control
		Proximal sensing
		Proximal crop sensors
			Proximal sensors for crop biotic stresses
				Weed detection
				Plant disease detection in field conditions
				Disease detection using light reflection
				Smartphone apps for proximal sensing of biotic stresses
				Proximal sensors to measure crop abiotic stresses
			Basic principles of visible, near infrared and mid infrared spectroscopy
			X-ray fluorescence (XRF) spectroscopy
			Gamma ray spectroscopy
		Remote sensing
			Remote earth observation for crop diseases identification
			Weed detection
			Detection of insects
			Crop disease detection
			Remote sensing for crop abiotic stresses
			Remote sensing for prediction of yield productivity
				Yield and biomass prediction
				Vegetation height
				Leaf area index (LAI) parameter
		Spectral and thermal properties of plants in a nutshell
			Spectral analysis methods to estimate N and water status in crop
			Nitrogen crop status
			Canopy/leaf relative water content (CRWC/LRWC)
			Thermal image processing to predict water content in crops
			Decision support for fertilization and irrigation regimes based on augmentation on remote sensed data
	Data fusion background
		Taxonomy of fusion architectures
		Data fusion advantages
	Data fusion applications in precision agriculture
		Fusion of optical images and synthetic aperture radar (SAR) approaches
		Fusion of light detection and ranging (LiDAR) data and images
		Fusion of optical images and GIS data
		Data fusion of satellite, aerial and close-range images
		Optical and fluorescence imaging
	References
	Further reading
4
Tutorial I: Weed detection
	Introduction
	Materials and methods
		Defining hyperspectral versus multispectral imaging
		Advantages of hyperspectral imaging
		Disadvantages of hyperspectral imaging
		Hyperspectral imaging applications in agriculture
		Experimental setup
	Explanation of experiments
		Plant selection
		NDVI and spatial resolution
	Results and discussion for weed detection
		Results for hierarchical maps
		Results for active learning
		Discussion on SOM based models and active learning
		Discussion on SOM based models and active learning
	References
5
Tutorial II: Disease detection with fusion techniques
	Introduction
		Optical sensing and their contribution to crop disease identification
		Artificial intelligence approaches for crop disease monitoring
		Reference and advanced optical methods for plant disease detection
		Combination of optical sensing with data mining algorithms
	Experimental setup
	Optical instrumentation
	Fusion of optical sensing data
	Results and discussion
		Results and discussion for LSSVM and ARD
		Results and discussion for active learning
	References
	Further reading
6
Tutorial III: Disease and nutrient stress detection
	Introduction
		Yellow rust disease detection
		Machine learning in crop status recognition
	Materials and methods
		Active learning scheme
		Results and discussion for active learning
	Results and discussion for hierarchical self organizing classifiers
	References
	Further reading
7 Tutorial IV: Leaf disease recognition
	Introduction
	State of the art
	Materials and methods
	Application of LBP in disease recognition of infected plants
		Image segmentation
		Creation of LBP histogram
		One class classification
			One class support vector machines (OCSVMs)
			One-class support-vector-learning for multi class problems
			Nearest-support-vector strategy
		Generalization of the vines powdery mildew model to different crop diseases
			Validation of the developed disease classification framework
			The generalization potential of one class SVM from vines powdery mildew to other plant diseases
			Generalization of the vines black rot model to different crops diseases
	Segmentation technique
	Classification process and the features extraction process
	References
8 Tutorial V: Yield prediction
	Introduction
	Materials and methods
	Experimental setup
		Crop parameters affecting yield
		Soil parameters affecting yield
		Prediction of crop yield
	Results and discussion
		Accuracy of yield prediction with supervised models
		Yield maps
	References
9 Tutorial VI: Postharvest phenotyping
	Introduction
	Experimental setup
	Fluorescence parameters
	Data analysis
		FluorPen 100 fluorescence parameters
	Results and discussion
	References
10 General overview of the proposed data mining and fusion techniques in agriculture
	Practical benefits for agriculture field
	Economic benefits for agricultural production
	Social benefits for agricultural production
		Environmental impact derived from the proposed methods
		Standardization framework and traceability
			Food safety
			Chemical residues
			Microbial control
			Toxin safety
			Relation of mycotoxin transmission to crop management practices
			Weed control
		Employment
		Food quality, geo-traceability and authenticity
		Health
		Development of skill/education and training
		Other socio-economic impacts
	Prospects for further development of the proposed sensing techniques in the near future
		Impacts of the projected methods to the surroundings
		Foresights of the projected techniques integrated within the industry
			Preparing for the fourth industrial revolution
	References
	Further reading
Index
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