UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

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Development and Application of Image-Based High-Throughput Phenotyping Methodology for Salt Tolerance in Lentils

Agronomy ◽

10.3390/agronomy10121992 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1992

Author(s):

Ruwani Dissanayake ◽

Hossein V. Kahrood ◽

Adam M. Dimech ◽

Dianne M. Noy ◽

Garry M. Rosewarne ◽

...

Keyword(s):

Salt Tolerance ◽

High Throughput ◽

Phenotypic Trait ◽

Screening Methods ◽

Toxicity Screening ◽

Salt Level ◽

Optimized Protocol ◽

High Throughput Phenotyping ◽

Modern Breeding ◽

Salt Toxicity

Soil salinity is a major abiotic stress in Australian lentil-producing areas. It is therefore imperative to identify genetic variation for salt tolerance in order to develop lentil varieties suitable for saline soils. Conventional screening methods include the manual assessment of stress symptoms, which can be very laborious, time-consuming, and error-prone. Recent advances in image-based high-throughput phenotyping (HTP) technologies have provided unparalleled opportunities to screen plants for a range of stresses, such as salt toxicity. The current study describes the development and application of an HTP method for salt toxicity screening in lentils. In a pilot study, six lentil genotypes were evaluated to determine the optimal salt level and the growth stage for distinguishing lentil genotypes using red–green–blue (RGB) images on a LemnaTec Scanalyzer 3D phenomics platform. The optimized protocol was then applied to screen 276 accessions that were also assessed earlier in a conventional phenotypic screen. Detailed phenotypic trait assessments, including plant growth and green/non-green color pixels, were made and correlated to the conventional screen (r = 0.55; p < 0.0001). These findings demonstrated the improved efficacy of an image-based phenotyping approach that is high-throughput, efficient, and better suited to modern breeding programs.

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<i>UAV-based high throughput phenotyping in specialty crops utilizing artificial intelligence</i>

10.13031/aim.201900293 ◽

2019 ◽

Author(s):

Yiannis Ampatzidis ◽

Victor Partel

Keyword(s):

Artificial Intelligence ◽

High Throughput ◽

Specialty Crops ◽

High Throughput Phenotyping

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High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging

IEEE Instrumentation & Measurement Magazine ◽

10.1109/mim.2017.7951684 ◽

2017 ◽

Vol 20 (3) ◽

pp. 4-12 ◽

Cited By ~ 11

Author(s):

Aaron Patrick ◽

Sara Pelham ◽

Albert Culbreath ◽

C. Corely Holbrook ◽

Ignacio Jose De Godoy ◽

...

Keyword(s):

High Throughput ◽

Multispectral Imaging ◽

Wilt Disease ◽

Unmanned Aerial Systems ◽

High Throughput Phenotyping ◽

Aerial Systems

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A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize

10.1101/2021.05.24.445447 ◽

2021 ◽

Author(s):

Jinliang Yang ◽

Eric Rodene ◽

Gen Xu ◽

Christine Smith ◽

Yufeng Ge ◽

...

Keyword(s):

Time Series ◽

High Throughput ◽

Pearson Correlation ◽

Phenotypic Trait ◽

Phenotypic Traits ◽

Time Points ◽

Canopy Coverage ◽

Genetic Components ◽

Genome Wide ◽

High Throughput Phenotyping

Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area (R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight --- a yield component trait (R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 (high chlorophyll fluorescence 244), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

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Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

Remote Sensing ◽

10.3390/rs11212494 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2494 ◽

Cited By ~ 5

Author(s):

Alem Gebremedhin ◽

Pieter Badenhorst ◽

Junping Wang ◽

Khageswor Giri ◽

German Spangenberg ◽

...

Keyword(s):

Plant Height ◽

High Throughput ◽

Perennial Ryegrass ◽

Multispectral Imaging ◽

Breeding Program ◽

Coefficient Of Determination ◽

Percentage Error ◽

Sensor Calibration ◽

Herbage Yield ◽

High Throughput Phenotyping

Sensor-based phenotyping technologies may offer a non-destructive, high-throughput and efficient assessment of herbage yield (HY) to replace current inefficient phenotyping methods. This paper assesses the feasibility of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate HY of single plants in a large perennial ryegrass breeding program. For sensor calibration, fresh HY (FHY) and dry HY (DHY) were acquired destructively, and plant height was measured at four dates each in 2017 and 2018 from a selected subset of 480 plants. Global multiple linear regression models based on K-fold and random split cross-validation methods were used to evaluate the relationship between observed vs. predicted HY. The coefficient of determination (R2) = 0.67–0.68 and a root mean square error (RMSE) between 5.43–7.60 g was obtained for the validation of predicted vs. observed DHY. The mean absolute error (MAE) and mean percentage error (MPE) ranged between 3.59–5.44 g and 22–28%, respectively. For the FHY, R2 values ranged from 0.63 to 0.70, with an RMSE between 23.50 and 33 g, MAE between 15.11 and 24.34 g and MPE between ~22% and 31%. Combining NDVI and plant height is a robust method to enable high-throughput phenotyping of herbage yield in perennial ryegrass breeding programs.

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Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

Frontiers in Plant Science ◽

10.3389/fpls.2021.768742 ◽

2022 ◽

Vol 12 ◽

Author(s):

Jing Zhou ◽

Eduardo Beche ◽

Caio Canella Vieira ◽

Dennis Yungbluth ◽

Jianfeng Zhou ◽

...

Keyword(s):

High Throughput ◽

Multispectral Imaging ◽

Large Population ◽

Image Features ◽

Environment Interaction ◽

Breeding Programs ◽

Soybean Yield ◽

Yield Trial ◽

High Throughput Phenotyping ◽

Selection Of

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

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