Deep Learning for Sustainable Agriculture

Front Matter
Copyright
Contributors
Smart agriculture: Technological advancements on agriculture-A systematical review
Introduction
Methodology
Role of image processing in agriculture
Plant disease identification
Fruit sorting and classification
Plant species identification
Precision farming
Fruit quality analysis
Crop and land assessment
Weed recognition
Role of Machine Learning in Agriculture
Yield prediction
Disease detection
Weed recognition
Crop quality
Species recognition
Soil management
Role of deep learning in agriculture
Leaf disease detection
Plant disease detection
Land cover classification
Crop type classification
Plant recognition
Segmentation of root and soil
Crop yield estimation
Fruit counting
Obstacle detection
Identification of weeds
Prediction of soil moisture
Cattle race classification
Role of IoT in agriculture
Climate condition monitoring
Crop yield
Soil patter
Pest and crop disease monitoring
Irrigation monitoring system
Optimum time for plant and harvesting
Tracking and tracing
Farm management system
Agricultural drone
Role of wireless sensor networks in agriculture
Irrigation management
Soil moisture prediction
Precision farming
Climate condition monitoring
Role of data mining in agriculture
Irrigation management
Prediction and detection of plant diseases
Pest monitoring
Optimum management of inputs (fertilizer and pesticides)
Crop yield prediction
Climate condition monitoring
Conclusion
References
A systematic review of artificial intelligence in agriculture
Precision farming
Introduction
Related work using AI
Objective and design consideration
Challenges and future scope
Plant disease detection
Introduction
Deep learning in image processing
Review of plant disease detection using image processing and deep learning
Performance analysis of some state-of-art techniques
Research gaps and future scope
Soil health monitoring using AI
Introduction
Brief history
Opportunity of AI in soil health monitoring
Current status
Scope and challenges of AI in agriculture
Conclusions
References
Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial veh ...
Introduction
Deep learning overview
CNN training
CNN in agricultural applications
Methodology
Data collection and processing
UAV specification
Image processing and labeling
Image processing
Data augmentation strategy
Software and hardware configuration
Experiment and results
Binary classification
Multiclass classification
Discussion
Advantages of the developed model
Conclusion
References
Design and implementation of a crop recommendation system using nature-inspired intelligence for Rajasthan, India
Introduction
Literature survey
Proposed methodology
Preprocessing layer
Download and tiling
Feature extraction
Image-based features
Sand and clay content
Knowledge-based features
Optimization layer
Plate tectonics optimization
BBO
Adaptive moment estimation optimization
PBO-BBO hybrid
PBO-Adam hybrid
Softmax classification layer
Results
Conclusion and future work
References
Further reading
Artificial intelligent-based water and soil management
Introduction
Applications of artificial intelligence in water management
Evapotranspiration estimation
Crop water content prediction
Water footprint modeling
Groundwater simulation
Pan evaporation estimation
Applications of artificial intelligence in soil management
Soil water content determination
Soil temperature monitoring
Soil fertilizer estimation
Soil mapping
Conclusion and recommendations for water-soil management
References
Machine learning for soil moisture assessment
Introduction
Overview of machine learning
Machine learning algorithms applied in soil moisture research
Linear regression
Artificial neural network/deep neural network
Support vector machine
Classification and regression tree
Random forest
Extremely randomized trees
Applications of machine learning for soil moisture assessment
Pedotransfer functions
Prediction models for soil moisture estimation/forecasting
Soil moisture retrieval through remote sensing
Irrigation scheduling
Downscaling of satellite-derived soil moisture products
Conclusions
Abbreviations
References
Automated real-time forecasting of agriculture using chlorophyll content and its impact on climate change
Introduction
Current status
National Status
International status
Problem statement
Objective of the proposed work
Research highlights
Scientific significance of the proposed work
Materials and methods
Histogram of oriented gradients
Principal component analysis
Backpropagation algorithm
Detailed work plan to achieve the objectives
Methodology
Results and discussion
Conclusion
References
Transformations of urban agroecology landscape in territory transition
Introduction
Agroecological landscapes
Agroecological practices
Agroecological territorial transformation and transition
Conclusion
References
WeedNet: A deep neural net for weed identification
Introduction
Related work
WeedNet
Model architecture
Complexity analysis
Evaluation strategy
Performance metrics
AUC
Precision
Recall
Accuracy
Data set
Experimental setup
Experimental evaluation
Conclusion
References
Sensors make sense: Functional genomics, deep learning, and agriculture
Introduction
Section I. Functional genomics
The emerging applications of soil microbial metabolites
Agricultural-based metabolites to advance nutraceutical production and drug discovery
Marine microalgae, aquaculture, and the DL toolbox Ludwig
Pollinators, Ludwig combiners, and the carbon-energy cycle
Section II. DAS networks
Agricultural factors in the plant-silicon cycle: Genomic regulation of blight, drought, and invasive species
Helically wound DAS
Section III. GRANITE and the agent-based GRANITE Network Discovery Tool
Conclusions
Acknowledgments
References
Crop management: Wheat yield prediction and disease detection using an intelligent predictive algorithms and ...
Introduction
Literature review
Wheat yield prediction
Genotypexenvironment interaction for wheat yield prediction
Machine learning algorithms for wheat yield prediction
Remote and satellite data for wheat yield prediction
CERES-Wheat model for wheat yield prediction
Evapotranspiration and soil moisture content for wheat yield prediction
Wheat diseases detection
Machine learning algorithms for wheat diseases detection
Web-based system with multiple regression for wheat disease detection
Image-processing techniques for wheat disease detection
Discussion
Conclusion and future scope
References
Sugarcane leaf disease detection through deep learning
Introduction
Methodology
Dataset
Leaf disease detection system architecture
Leaf disease detection model architecture
SAFAL-FASAL android application
Method of evaluation
Experimentation
Results and discussion
Performance evaluation
SAFAL-FASAL Android application results
Conclusion
References
Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture
Introduction
Applications of geospatial analytics for agriculture
Importance of remote sensing to estimate paddy area
Related studies based on satellite imaginary
Applications with machine learning approaches
Applications with deep learning approaches
Related studies based on the Internet of Things
Related studies with integrated data
Dataset associated with land-use land-cover data
Comparison of related studies with satellite imagery and deep learning
Material analysis
Data source
Analysis of raster data
System model design and implementation
Process view
Data preprocessing and feature selection
Transfer learning process
System evaluation
Model evaluation
Ground truth measurement
Model prediction comparison for contextual analysis
Discussion
Contribution of the proposed study
Limitations of the datasets
Future research directions
Conclusions
References
Artificial intelligence-based detection and counting of olive fruit flies: A comprehensive survey
Introduction
Literature survey of recognition systems
Manual detection and counting
Semiautomatic detection and counting
Automatic detection and counting
Artificial neural networks
Deep learning via CNNs
Image processing
Optoacoustic spectral analysis
Spectroscopy hyperspectral imaging
Evaluation and discussions
Semiautomatic detection
Image-based automatic detection
Machine and deep learning
Image processing
Nonimage-based automatic detection
Conclusions
Acknowledgments
References
Index