About this Research Topic

Background

Improvements in agronomical practices to meet the global food security challenges are paramount responses under changing climatic scenarios. Precise and accurate measurement of plant traits especially in field scale plays an important role in the genetic improvement of crop plants. Field-based phenotyping (FBP) is a critical component of crop improvement through plant genetics, as it is the ultimate expression of the relative effects of genetic factors, environmental factors, and their interaction on critical production traits, such as yield potential and tolerance to abiotic/biotic stresses. FBP is increasingly recognized as the only approach capable of delivering the required throughput and an accurate description of plant trait expression in real-world cropping systems.

With the fast development of reliable, automatic, multifunctional platforms for high-throughput FBP, such as Unmanned Ground Vehicle (UGV), the Cable-Suspended Vehicle (CSV), Unmanned Aerial Vehicle (UAV), the bottleneck of plant phenotyping will be relieved to offer plant scientists new insight into all the aspects of living plants. This research topic is a unique attempt to simultaneously tackle optical and radar sensors, high-throughput phenotyping platform development, remote sensing big data analysis algorithms, quantitative inversion or evaluation models of plant traits, as well as the integration of UGV, CSV, UAV, sensors and algorithmic applications. These non-invasive FBPs have been used to estimate leaf area index (LAI), canopy cover, nitrogen content, leaf pigment, biomass, plant structure, plant density, phenology, leaf health, canopy/leaf water content and temperature, and abiotic/biotic stress. Remote sensing techniques of FBPs have become much more advanced to pave the way in harnessing the potentiality of genomic resources in genetic improvement of crop plants. These techniques will also help in finding more relevant solutions for the major problems that are currently limiting crop production.

This research topic is aiming at showing the latest technical developments in FBP from remote sensing point of view. We welcome innovative contributions of original research articles, and also review articles, as well as opinion papers, and papers on perspectives and on novel methods of remote sensing phenotyping. Contributions will cover four aspects:
(i) innovative FEB and optical/radar sensors;
(ii) remote sensing data fusion and big data mining modelling;
(iii) quantitative inversion or evaluation models of plant traits, and
(iv) application and assessment of FBP for different plants.

Keywords: Hyperspectral imagery, Multispectral imagery, Thermal infrared imagery, Lidar sensor, Sensor integration, Field-based phenotyping platform, Spectral and spatial data fusion, Remote sensing data mining, Inversion modeling, Crop growth modelling, Plant traits, Pest and diseases, Photosynthesis and productivity

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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