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

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Matthew Maimaitiyiming
Website1 Website2
Guest Editor
Division of Food Systems and Bioengineering, University of Missouri-Columbia,256 WC Stringer Wing, Eckles Hall, Columbia, Missouri 65211, USA
Interests: Hyperspectral remote sensing; remote estimation of plant biochemistry and physiology; plant stress detection; spatial statistics; Precision Agriculture; urban heat island; Spectroscopy
Dr. Sidike Paheding

Guest Editor
Purdue University Northwest, 2200 169th St, Hammond, IN 46323, USA
Interests: deep learning; computer vision; remote sensing
Special Issues and Collections in MDPI journals
Assoc. Prof. Dr. Vasit Sagan
Website
Guest Editor
Department of Earth & Atmospheric Sciences, Saint Louis University, Des Peres Hall 203B, 3694 West Pine Mall, St. Louis, MO 63108 USA
Interests: remote sensing, GIS, AI/machine learning, sensor/information fusion, geospatial methods
Special Issues and Collections in MDPI journals

Special Issue Information

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

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.

Keywords

  • 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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessFeature PaperArticle
Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning
Remote Sens. 2020, 12(13), 2082; https://doi.org/10.3390/rs12132082 - 29 Jun 2020
Abstract
Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and [...] Read more.
Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models. Full article
Show Figures

Graphical abstract

Open AccessArticle
Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
Remote Sens. 2020, 12(9), 1357; https://doi.org/10.3390/rs12091357 - 25 Apr 2020
Abstract
Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of [...] Read more.
Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring. Full article
Show Figures

Figure 1

Back to TopTop