Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

Xu, Rui; Li, Changying; Bernardes, Sérgio

doi:10.3390/rs13173517

Cited by 26 publications

(10 citation statements)

References 55 publications

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“…A model incorporating both plant height and canopy cover was created to predict yield. Xu et al (2021) developed an unmanned aerial system integrating three types of imaging sensors (RGB, multispectral, and thermal) as well as a LiDAR sensor to monitor cotton morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Validation tests in a cotton field showed the multisensor aerial system to be a useful tool for cotton breeding and precision management.…”

Section: Plant Growthmentioning

confidence: 99%

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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Section: Plant Growthmentioning

confidence: 99%

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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“…Using machine vision, picture segmentation, and big data processing technologies to reliably gather and analyze crucial plant traits is an important technical means for the development of contemporary agriculture, with significant guiding value for crop management and genetic breeding (Granier and Vile, 2014 ; Li et al, 2021 ). Scholars have conducted field studies on the morphological indicators of dicotyledonous crops, including stem height (Paproki et al, 2012 ), plant height (Sun et al, 2017 ), leaf width (Paproki et al, 2012 ), leaf length (Paproki et al, 2012 ), number of leaves (Dobrescu et al, 2020 ), canopy coverage (Kirchgessner et al, 2016 ; Borra-Serrano et al, 2020 ; Wan et al, 2021 ; Xu et al, 2021 ), canopy height (Kirchgessner et al, 2016 ; Borra-Serrano et al, 2020 ), canopy roughness (Herrero-Huerta et al, 2020 ), and flowers (Xu et al, 2017 ; Jiang et al, 2020 ).…”

Section: Research Status Of High-throughput Phenotypic Information Of...mentioning

confidence: 99%

“…Moreover, similar research has been conducted on cotton, rape, and other crops. Xu et al ( 2021 ) created a UAV system with three cameras (RGB, multispectral, and thermal) and a lidar sensor to identify cotton canopy coverage and canopy height, with an average relative error of only 6.6%. The approach of collecting crop morphological data from RGB images is quite accurate.…”

Section: Research Status Of High-throughput Phenotypic Information Of...mentioning

confidence: 99%

“…Because agriculture is currently facing resource shortages and serious farmland environmental pollution, the implementation of precision agriculture demonstrations and research is critical. High-throughput phenotyping platforms can be used to obtain crop image traits and conduct modeling to estimate the yield and quality of a crop (Xu et al, 2021 ) and to analyze the relationship between crop growth and environmental factors, thereby enabling more precise management (Maimaitijiang et al, 2019 ).…”

Section: Research Status Of High-throughput Phenotypic Information Of...mentioning

confidence: 99%

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The field phenotyping platform's next darling: Dicotyledons

Chen

et al. 2022

Front. Plant Sci.

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The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

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“…Infrared thermography is increasingly used in non-destructive condition monitoring services as a less labor-intensive and non-contact technique to assess vegetation stress by leaf canopy temperature [ 12 , 13 , 14 , 15 ]. Canopy temperature has been widely used to detect stress because the closing of the stomas on the leaves is controlled to retain water during the stress, resulting in changes in stomatal conductance, transpiration, and leaf temperature [ 16 , 17 , 18 , 19 , 20 , 21 ]. Some research found that the canopy temperature between the stressed vegetation and healthy vegetation was different [ 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images

Xiong

Jiang

Pan

et al. 2022

Sensors

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The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.

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Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

Cited by 26 publications

References 55 publications

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

The field phenotyping platform's next darling: Dicotyledons

Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images

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