Optimization of temporal UAS‐based imagery analysis to estimate plant maturity date for soybean breeding

Volpato, Leonardo; Dobbels, Austin; Borém, Aluízio; Lorenz, Aaron J.

doi:10.1002/ppj2.20018

Cited by 13 publications

(19 citation statements)

References 58 publications

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“…We compare the performance of our proposed deep learning model against a recent state-of-the-art benchmark created by Volpato et al (2021) where a local regression model is fitted to the RGB color transformation values over time. That is, for each plot and drone flight date, an RGB transformation is performed, and the output is fed into the regression model.…”

Section: Benchmarkmentioning

confidence: 99%

“…That is, for each plot and drone flight date, an RGB transformation is performed, and the output is fed into the regression model. Volpato et al (2021) demonstrated that using the mean greenness leaf index (GLI) on each plot combined with locally estimated scatter plot smoothing (LOESS) regression results in higher correlations between the predicted and ground truth maturity days.…”

Section: Benchmarkmentioning

confidence: 99%

“…Finally, the relative maturity date is predicted as the day with the closest value to a predefined threshold, where the threshold value corresponds to a level of greenness. Volpato et al (2021) suggested that a GLI threshold of 0.02 was optimal from their extensive testing. However, this value can vary based on the environment and equipment, and optimizing a single threshold is not scalable.…”

Section: Benchmarkmentioning

confidence: 99%

“…Also given that commercial corn is a staple crop around the world due to being used for animal feed and biofuels, it is no surprise that there is substantial work combining corn images and deep learning (Khaki et al, 2020a(Khaki et al, ,b, 2021a. Aside from deep learning, many other researchers have combined traditional statistical modeling, machine learning, and image processing with agricultural data (Singh et al, 2016;Naik et al, 2017;Moeinizade, 2018;Dobbels and Lorenz, 2019;Moeinizade et al, 2020b;Pothen and Pai, 2020;Li et al, 2020;Volpato et al, 2021;Shahhosseini et al, 2019Shahhosseini et al, , 2021. With the rapid combination of analytical techniques and plant breeding, this large body of recent work illustrate that large-scale analysis of imagery for improved decision making is possible.…”

mentioning

confidence: 99%

“…Recent efforts have used drone-based imagery to estimate the days to soybean maturity, that is, the day of the year in which 95% of pods within a plot have turned to their mature brown color. These approaches have relied on multiple linear regression (Christenson et al, 2016), LOESS regression (Volpato et al, 2021), segmented regression (Narayanan et al, 2019), and partial least squares regression (Zhou et al, 2019). Each of these methods relies on condensing each RGB image to a single value, such as the normalized difference vegetation index or the green leaf index.…”

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confidence: 99%

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An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions

Moeinizade¹,

Pham²,

Ye³

et al. 2021

Preprint

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For a global breeding organization, identifying the next generation of superior crops is vital for its success.Recognizing new genetic varieties requires years of in-field testing to gather data about the crop's yield, pest resistance, heat resistance, etc. At the conclusion of the growing season, organizations need to determine which varieties will be advanced to the next growing season (or sold to farmers) and which ones will be discarded from the candidate pool. Specifically for soybeans, identifying their relative maturity is a vital piece of information used for advancement decisions. However, this trait needs to be physically observed, and there are resource limitations (time, money, etc.) that bottleneck the data collection process. To combat this, breeding organizations are moving toward advanced image capturing devices. In this paper, we develop a robust and automatic approach for estimating the relative maturity of soybeans using a time series of UAV images. An end-to-end hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) is proposed to extract features and capture the sequential behavior of time series data. The proposed deep learning model was tested on six different environments across the United States. Results suggest the effectiveness of our proposed CNN-LSTM model compared to the local regression method. Furthermore, we demonstrate how this newfound information can be used to aid in plant breeding advancement decisions.

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Section: Benchmarkmentioning

confidence: 99%

Section: Benchmarkmentioning

confidence: 99%

Section: Benchmarkmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions

Moeinizade¹,

Pham²,

Ye³

et al. 2021

Preprint

Self Cite

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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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Heading percentage estimation in proso millet (Panicum miliaceum L.) using aerial imagery and deep learning

Zhao

Khound

Ghimire

et al. 2022

The Plant Phenome Journal

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Proso millet (Panicum miliaceum L.), one of the major cultivated millets, serves as a complement to major cereal crops due to its drought tolerance and low input demands. Timing of heading is one of the key agronomic traits associated with its adaptation to a target environment and a major focus in breeding. Conventionally, heading percentage of a plot (genotype) was rated visually by breeders in field. Despite many successful studies reported in automatic head detections in other small grain species, little progress had been made to estimate heading percentage especially when multiple tillers exist. This study aimed to develop a method for automatic proso millet panicle detection and, more importantly, heading percentage estimation using regular red‐green‐blue images collected by an unmanned aerial vehicle. Aerial images of two dates were collected at heading stage in 2020 in Scottsbluff, NE. Faster regions with convolutional neural network models were trained to detect and count proso millet panicles in each plot. Then, using a sigmoid model, the number of detected panicles was converted to heading percentage without having the information of stand count and the number of tillers. Overall, the system achieved the highest coefficient of determination of 0.728 for proso millet heading percentage estimation, and an accuracy of 92.4% in determining whether a plot reached a certain threshold of heading (50% in this study). The methods developed in this study on heading percentage estimation can directly aid in decision making in proso millet breeding and can be ultimately incorporated into an automated proso millet high‐throughput phenotyping pipeline.

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Optimization of temporal UAS‐based imagery analysis to estimate plant maturity date for soybean breeding

Cited by 13 publications

References 58 publications

An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions

An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions

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

Heading percentage estimation in proso millet (Panicum miliaceum L.) using aerial imagery and deep learning

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