Dear Colleagues,

The growing demand for food production triggered by exponential population growth has prompted research for improving agricultural production and protecting natural resources. Timely and accurately monitoring crop growth and performance is important for decision making in precision agriculture, as well as ensuring food security. Proximal and remote sensing from ground-, airborne-, and satellite-based platforms have been increasingly used as a precision technology and providing methodologies for crop management.

Multiscale data recorded by different sensors, including multi-/hyper-spectral, RGB, thermal, LiDAR, and radar can be integrated to obtain the most efficient usage of rich spectral, spatial, structural, thermal, and temporal information contained in diverse sensor systems and platforms. Additionally, advanced machine learning and deep learning have emerged as effective alternatives with robust learning abilities in relation to the highly complex sensor data at hand. However, the development of successful deep learning models depends on a massive amount of field sampling, which is not ideal for the community. Extreme learning machine (ELM), a single-layer feed-forward neural network, has been found to be accurate and computationally efficient, while providing comparable results with state-of-the-art algorithms in a variety of research fields. To unravel potential and better understand challenges in application of ELM for multiscale and multimodal sensor data in an agricultural setting, solicited topics for this special issue include but are not limited to the following:

• ELM and data fusion to improve crop phenotypic trait estimation and yield prediction
• The application of ELM and multiscale remote sensing data for crop disease mapping and detection
• Deep ELM for precision agriculture
• Object/target/anomaly detection in remote sensing images using ELM
• ELM for land-cover/land-use mapping
• ELM for crop species mapping
• Parallel and distributed computing of ELM.
• Improved ELM variants for crop monitoring using proximal and remote sensing
• Evaluation of ELM performance with respect to alternative methods for agricultural applications
• Time-series processing of large scale remote sensing data with ELM

Related References

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• Extreme learning machine
• Machine learning
• Deep learning
• Proximal/remote sensing
• Aerial and satellite images
• Multimodality data fusion
• Precision agriculture
• Phenotyping
• Crop traits
• Crop assessment and mapping