Special Issue "Extreme Leaning Machine (ELM) for Agriculture Using Proximal and Remote Sensing Data"
Deadline for manuscript submissions: 31 December 2020.
Interests: Hyperspectral remote sensing; remote estimation of plant biochemistry and physiology; plant stress detection; spatial statistics; Precision Agriculture; urban heat island; Spectroscopy
Interests: deep learning; computer vision; remote sensing
Special Issues and Collections in MDPI journals
Interests: remote sensing, GIS, AI/machine learning, sensor/information fusion, geospatial methods
Special Issues and Collections in MDPI journals
Special Issue in Sensors: Advances in Remote Sensing of Land-Cover and Land-Use Changes
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
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Visiting Assist. Prof. Dr. Sidike Paheding
Assoc. Prof. Dr. Vasit Sagan
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Extreme learning machine
- Machine learning
- Deep learning
- Proximal/remote sensing
- Aerial and satellite images
- Multimodality data fusion
- Precision agriculture
- Crop traits
- Crop assessment and mapping