Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery

Dear Colleagues,

To meet the global food security challenges under changing climatic scenarios, it is most important to enhance crop yield under resource competence. Accurate and precise measurements of crop phenotyping traits play an important role in harnessing the potentiality of genomic resources in the genetic improvement of crop yield. In traditional crop phenotyping, traits are assessed with statistical analysis methods, which must be done manually. Human effort, time, and resources are needed to measure plant characteristics. With the fast development of Unmanned Ground Vehicle (UGV), Unmanned Aerial Vehicle (UAV), sensor technologies, and image algorithms, the integration of UGV, UAV, sensors and algorithmic applications for automatic crop phenotyping are being handled to overcome the defects of manual techniques. These high-throughput non-invasive crop phenotyping platforms have been used to estimate LAI, canopy cover, nitrogen, chlorophyll, biomass, plant structure, plant density, phenology, leaf health, canopy/leaf temperature, and the physiological state of photosynthetic machinery under different stress conditions. They have become much more advanced in order to provide a solution to genomics-enabled improvements and address our need of precise and efficient phenotyping of crop plants. They will also help in finding more relevant solutions for the major problems that are currently limiting crop production.

This Special Issue is focused on the latest innovative research results in the field of remote sensing technology, senor technologies, and imagery algorithm development and applications specifically addressing issues estimating the crop phenotyping traits based on UGV and UAV imagery. The list below provides a general (but not exhaustive) overview of the topics that are solicited for this Special Issue:

Ø  UGV and UAV platforms application for crop phenotyping traits

Ø  Imagery algorithms (data fusion, segmentation, classification, machine learning, and deep learning, etc.) to estimate crop phenotyping traits

Ø  Sensors (RGB, multispectral, hyperspectral, thermal, Lidar, fluorescence, etc.) application for crop phenotyping traits

Ø  Combination of different sensors data to improve the estimation accuracy of crop phenotyping traits

Ø  Data assimilation of multisource images into two- or three-dimensional crop models

Dr. Xiuliang Jin
Dr. Zhenhai Li
Prof. Dr. Clement Atzberger
Guest Editors

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