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Plants
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Plant Phenomics for Precision Agriculture
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Plant Phenomics for Precision Agriculture

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".

Deadline for manuscript submissions: closed (1 April 2024) | Viewed by 7261

Special Issue Editor

Dr. Yong Suk Chung

E-Mail Website
Guest Editor
Department of Plant Resouces and Environment, Jeju National University, Jeju 63243, Republic of Korea
Interests: phenomics; precision agriculture; plant breeding; smart farm; germplasm enhancement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant phenomics has been heavily applied for plant breeding ever since its advent in the form of high throughput phenotyping using various methods. However, it has huge potential for smart farm in both greenhouse and field, field managements, yield prediction, etc., which are in the precision agricultural category.

This Special Issue of Plants seeks to stimulate and collect studies in precision agriculture using phenomics. Any topic or technology including high throughput phenotyping, field management, plant breeding, analysis methods, field heterogeneity, remote sensing, image analysis, vegetative index, or smart farm will be considered for publication as long as it is related to precision agriculture combined with plant phenomics. Conceptualization for potential application using phenomics for precision agriculture will also be considered for publication.

Dr. Yong Suk Chung
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 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. Plants 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 2700 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

  • phenomics
  • precision agriculture
  • high throughput phenotyping
  • field management
  • plant breeding
  • analysis methods
  • field heterogeneity
  • remote sensing
  • image analysis
  • vegetative index
  • smart farm

Published Papers (4 papers)

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Research

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20 pages, 5000 KiB  
Article
A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation
by David Gómez-Candón, Joaquim Bellvert, Ana Pelechá and Marta S. Lopes
Plants 2023, 12(22), 3871; https://doi.org/10.3390/plants12223871 - 16 Nov 2023
Cited by 1 | Viewed by 874
Abstract
This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were [...] Read more.
This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions. Full article
(This article belongs to the Special Issue Plant Phenomics for Precision Agriculture)
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20 pages, 7495 KiB  
Article
Comparison of Various Drought Resistance Traits in Soybean (Glycine max L.) Based on Image Analysis for Precision Agriculture
by JaeYoung Kim, Chaewon Lee, JiEun Park, Nyunhee Kim, Song-Lim Kim, JeongHo Baek, Yong-Suk Chung and Kyunghwan Kim
Plants 2023, 12(12), 2331; https://doi.org/10.3390/plants12122331 - 15 Jun 2023
Cited by 1 | Viewed by 900
Abstract
Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to [...] Read more.
Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories—area, boundary, and color—and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture. Full article
(This article belongs to the Special Issue Plant Phenomics for Precision Agriculture)
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15 pages, 3266 KiB  
Article
Heterogeneity Assessment of Kenaf Breeding Field through Spatial Dependence Analysis on Crop Growth Status Map Derived by Unmanned Aerial Vehicle
by Gyujin Jang, Dong-Wook Kim, Won-Pyo Park, Hak-Jin Kim and Yong-Suk Chung
Plants 2023, 12(8), 1638; https://doi.org/10.3390/plants12081638 - 13 Apr 2023
Cited by 1 | Viewed by 1024
Abstract
The investigation of quantitative phenotypic traits resulting from the interaction between targeted genotypic traits and environmental factors is essential for breeding selection. Therefore, plot-wise controlled environmental factors must be invariable for accurate identification of phenotypes. However, the assumption of homogeneous variables within the [...] Read more.
The investigation of quantitative phenotypic traits resulting from the interaction between targeted genotypic traits and environmental factors is essential for breeding selection. Therefore, plot-wise controlled environmental factors must be invariable for accurate identification of phenotypes. However, the assumption of homogeneous variables within the open-field is not always accepted, and requires a spatial dependence analysis to determine whether site-specific environmental factors exist. In this study, spatial dependence within the kenaf breeding field was assessed in a geo-tagged height map derived from an unmanned aerial vehicle (UAV). Local indicators of spatial autocorrelation (LISA) were applied to the height map using Geoda software, and the LISA map was generated in order to recognize the existence of kenaf height status clusters. The spatial dependence of the breeding field used in this study appeared in a specific region. The cluster pattern was similar to the terrain elevation pattern of this field and highly correlated with drainage capacity. The cluster pattern could be utilized to design random blocks based on regions that have similar spatial dependence. We confirmed the potential of spatial dependence analysis on a crop growth status map, derived by UAV, for breeding strategy design with a tight budget. Full article
(This article belongs to the Special Issue Plant Phenomics for Precision Agriculture)
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Review

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23 pages, 2187 KiB  
Review
Image-Based High-Throughput Phenotyping in Horticultural Crops
by Alebel Mekuriaw Abebe, Younguk Kim, Jaeyoung Kim, Song Lim Kim and Jeongho Baek
Plants 2023, 12(10), 2061; https://doi.org/10.3390/plants12102061 - 22 May 2023
Cited by 5 | Viewed by 3575
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the [...] Read more.
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives. Full article
(This article belongs to the Special Issue Plant Phenomics for Precision Agriculture)
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