Extreme Leaning Machine (ELM) for Agriculture Using Proximal and Remote Sensing Data

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

1. Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70, 489–501.
2. Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Peterson, J., Kadam, S., Burken, J., ​Fritschi, F. (2017). Unmanned aerial system-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134:43–58.
3. Maimaitiyiming, M., Sagan, V., Sidike, P., Kwasniewski, M. (2019). Dual activation function based Extreme Learning Machine (ELM) for estimating grapevine berry yield and quality. Remote Sensing, 11(7).
4. Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., Martinez, M. (2018). Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine. Remote Sens., 10(10), 1503; doi: 10.3390/rs10101503
5. Huang, G.; Huang, G.-B.; Song, S.; You, K. Trends in extreme learning machines: A review. Neural Netw. 2015, 61, 32–48.
6. Rocha Neto, O.; Teixeira, A.; Leão, R.; Moreira, L.; Galvão, L. Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation. Remote Sens. 2017, 9, 42.
7. Ghamisi, J. Plaza, Y. Chen, J. Li and A. J. Plaza, "Advanced Spectral Classifiers for Hyperspectral Images: A review," in IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 1, pp. 8–32, March 2017.
8. Essa, A.; Sidike, P.; Asari, V. Volumetric Directional Pattern for Spatial Feature Extraction in Hyperspectral Imagery. IEEE Geosci. Remote Sens. 2017, 14, 1056–1060.
9. Moreno, R.; Corona, F.; Lendasse, A.; Graña, M.; Galvão, L.S. Extreme learning machines for soybean classification in remote sensing hyperspectral images. Neurocomputing 2014, 128, 207–216.
10. Ravinesh C. Deo, Mehmet Şahin, Jan F. Adamowski, Jianchun Mi, Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach, Renewable and Sustainable Energy Reviews, Volume 104, 2019, 235–261
11. T. Le, D. Xiao, Y. Mao, D. He, J. Xu and L. Song, "Coal Quality Exploration Technology Based on an Incremental Multilayer Extreme Learning Machine and Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4192–4201, July 2019.
12. Contreras, M. Khodadadzadeh, P. Ghamisi and R. Gloaguen, "Mineral Mapping of Drill Core Hyperspectral Data with Extreme Learning Machines," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 2686–2689.
13. Lifeng Wu, Guomin Huang, Junliang Fan, Xin Ma, Hanmi Zhou, Wenzhi Zeng, Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction, Computers and Electronics in Agriculture, 2019, 105115
14. Ghimire, Sujan, et al. "Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities." Remote Sensing of Environment 212 (2018): 176–198.

Dr. Matthew Maimaitiyiming
Visiting Assist. Prof. Dr. Sidike Paheding
Assoc. Prof. Dr. Vasit Sagan
Guest Editor

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