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Remote Sensing, Big Data Integration, and Image Analyzing Methods for Accelerating Crop Improvement

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4755

Special Issue Editors

Dr. Mohsen Yoosefzadeh Najafabadi

E-Mail Website
Guest Editor
Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: plant breeding; bigdata; climate-based breeding; computational biology; data integration strategies; genomics; omics-based research; phenomics
Special Issues, Collections and Topics in MDPI journals
Dr. Keshav D Singh

E-Mail Website
Guest Editor
Agriculture and Agri-Food Canada (AAFC), Lethbridge Research Centre, 5403—1 Ave S., Lethbridge, AB, Canada
Interests: remote sensing; UAV imaging; plant phenomics; precision agriculture; crops mapping and big-data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Dessislava Ganeva

E-Mail Website
Guest Editor
Space Research and Technology Institute, Bulgarian Academy of Sciences, Georgi Gonchev bl1, 1113 Sofia, Bulgaria
Interests: remote sensing; biophysical variables retrieval; parametric and nonparametric models; phenotyping

Special Issue Information

Dear Colleagues,

Accelerating crop improvement is of paramount importance for sustainable food production and lifting the farming standard in the near future. Therefore, fast and non-destructive phenotyping tools should be adopted to measure the overall plant status in a large crop population. Remote/proximal sensing, as one of the most important tools in agriculture, plays an important role in monitoring plant health, predicting the overall crop status at early growth stages, and modifying crop management practices in a changing climate. However, the scale and complexity of these systems coupled with an increasingly high volume of data present significant challenges in keeping these systems up and running as they grow. Recently, data integration and computation strategies have been employed by data scientists and information engineers to combine and process different datasets from genomics to phenomics to gain in-depth knowledge about the traits of interest.

This Special Issue represents the latest advances in proximal/remote sensing, data integration, and big data analyzing methods to improve the genetic gain and accelerate the crop improvement rate. We invite authors to submit all types of manuscripts, including original research, research concepts, communications, and reviews mainly in (but not limited to) the following topics:

  • Crop breeding and phenotyping;
  • Precision agriculture and mapping;
  • Climate-resilient crops improvement;
  • Crops under hydroponic substrates;
  • Resource conservation and water uses;
  • Proximal, aerial, and satellite imaging;
  • Advanced sensor-based systems;
  • Computer vision, AI, and deep learning;
  • Big data and predictive analytics.

Dr. Mohsen Yoosefzadeh Najafabadi
Dr. Keshav D Singh
Dr. Dessislava Ganeva
Guest Editors

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 submissions that pass pre-check are 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. Sensors 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 2600 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

  • remote sensing
  • crop improvement
  • phenotyping
  • precision agriculture

Published Papers (2 papers)

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Research

23 pages, 9545 KiB  
Article
Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs
by Dessislava Ganeva, Eugenia Roumenina, Petar Dimitrov, Alexander Gikov, Georgi Jelev, Boryana Dyulgenova, Darina Valcheva and Violeta Bozhanova
Sensors 2023, 23(11), 5008; https://doi.org/10.3390/s23115008 - 23 May 2023
Cited by 3 | Viewed by 1636
Abstract
This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination [...] Read more.
This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley. Full article
(This article belongs to the Special Issue Remote Sensing, Big Data Integration, and Image Analyzing Methods for Accelerating Crop Improvement)
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22 pages, 18518 KiB  
Article
Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing
by Giorgio Morales, John W. Sheppard, Paul B. Hegedus and Bruce D. Maxwell
Sensors 2023, 23(1), 489; https://doi.org/10.3390/s23010489 - 02 Jan 2023
Cited by 7 | Viewed by 2144
Abstract
In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional [...] Read more.
In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional Neural Networks (CNNs) given their unique ability to exploit the spatial information of small regions of the field. We present a novel CNN architecture called Hyper3DNetReg that takes in a multi-channel input raster and, unlike previous approaches, outputs a two-dimensional raster, where each output pixel represents the predicted yield value of the corresponding input pixel. Our proposed method then generates a yield prediction map by aggregating the overlapping yield prediction patches obtained throughout the field. Our data consist of a set of eight rasterized remotely-sensed features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), aspect, and two radar backscatter coefficients acquired from the Sentinel-1 satellites. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present leave-one-out cross-validation experiments for rain-fed winter wheat over four fields and show that our proposed methodology produces better predictions than five compared methods, including Bayesian multiple linear regression, standard multiple linear regression, random forest, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures. Full article
(This article belongs to the Special Issue Remote Sensing, Big Data Integration, and Image Analyzing Methods for Accelerating Crop Improvement)
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