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

Patrick, Aaron; Pelham, S. D.; Culbreath, A. K.; Holbrook, C. Corely; Godoy, Ignácio José de; Li, Changying

doi:10.1109/mim.2017.7951684

Cited by 56 publications

(39 citation statements)

References 11 publications

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“…They were able to autonomously detect variations within fields of crops using RGB, infrared, and thermal cameras mounted to a UAS, which can reduce the time involved in phenotyping new lines in plant breeding operations [67]. In a similar study, tomato spotted wilt disease resistance phenotyping was conducted through an UAS using multispectral imaging and vegetation indices, which improved detection time to as early as 93 days after germination [68]. Tripicchio and his colleagues set out to improve large-scale analysis of cultivation techniques in which they successfully implemented a method to remotely detect field plowing depths without the use of satellite imaging [69].…”

Section: Miscellaneousmentioning

confidence: 99%

“…Most other applications are dominated by the rotorcrafts for their ease of use (no runway is required), lower cost, and high spatial resolution imaging due to the ability to hover as seen in Figure 1 [75]. Rotorcrafts come in many forms with any number of propellers, with the most common configurations including traditional helicopters [67]; quadcopters (4 propellers) [19,68]; hexacopters (6 propellers) [21,76]; and octocopters (8 propellers) [8,72]. When deciding which platform to build upon, it is important to check for max payload capability as well as any weather restrictions-such as whether or not the platform is waterproof-in order to fit the needs of the project.…”

Section: Platformsmentioning

confidence: 99%

“…The most common use of multispectral and NIR cameras in agriculture is for the creation of vegetation indices which relies on NIR data or specific bands of light [19,41,68,79,80]. Near infra-red cameras work similarly to RGB cameras with internal modifications or lens filters added to tweak the bands of light captured from the traditional RGB to incorporate other bands, usually from the NIR region [72,81].…”

Section: Multispectral and Nir Camerasmentioning

confidence: 99%

“…This can be useful as plants reflect light in a wider range of light than humans can detect with the naked eye. The majority of UAS-assisted aerial imaging for detection or estimation purposes uses multispectral imaging heavily to calculate vegetation indices such as NDVI and other variants involving NIR data [6,26,33,34,41,68]. A notable exception comes from an article by Al-Saddik and her colleagues which directly used multispectral data programmed to specific bands to detect Flavescence dorée disease in vineyards without using common indices [30].…”

Section: Multispectral and Nir Camerasmentioning

confidence: 99%

“…Vegetation indices are numerical values generated through relationships between different wavelengths of light reflected from plants [82]. Depending on the index, these values can be associated with particular properties of plant health and growth including canopy greenness, pigment composition, water stress, nutrient stress, photosynthetic potential, and diseased tissue [26,33,68]. While many common vegetation indices rely upon multispectral imaging to provide a NIR band, a number of useful indices can be calculated using strictly RGB cameras as seen in the work done by Fuentes-Peailillo and his colleagues in 2019.…”

Section: Vegetationmentioning

confidence: 99%

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Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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Section: Miscellaneousmentioning

confidence: 99%

Section: Platformsmentioning

confidence: 99%

Section: Multispectral and Nir Camerasmentioning

confidence: 99%

Section: Multispectral and Nir Camerasmentioning

confidence: 99%

Section: Vegetationmentioning

confidence: 99%

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Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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Utility of unmanned aerial systems for measuring plant height and plant vigor among several Georgia runner peanut cultivars

Brown

Zhang

Maleski

et al. 2022

The Plant Phenome Journal

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Stand establishment, early‐season seedling vigor, and mid‐season canopy closure ensure peanut growers’ timely achievement of a vigorous crop that can resist weeds, take advantage of available moisture and nutrients, and produce optimal yield. Direct measurement of these traits is prohibitively time consuming in large breeding trials and nurseries. Estimations based on visual ratings are rapid but require extensive training and experience for reliable data. Technological improvements in camera and unmanned aerial systems (UAS) has made them a practical and beneficial tool for plant breeders. This study evaluated the use of UAS for measuring plant growth‐related traits in a replicated yield trial of 16 Georgia runner‐type peanut cultivars. Flights were conducted at similar times with ground measurements and visual ratings of seedling and mid‐season plant vigor at the University of Georgia's Gibbs Research Farm in Tifton, GA, from 2019 to 2021. High correlations and similar ranks were seen between UAS‐derived and manual measurements for plant height among cultivars evaluated at mid‐season and late‐season (r2 = 0.95 and 0.75, respectively). For early‐season vigor evaluations, moderate correlations were achieved (r2 = 0.11–0.41), however, high correlations and similar results were evident comparing UAS‐derived mid‐season growth characteristics and visual vigor ratings (r2 = 0.75–0.86). Given the affordability and efficiency of data collection, UAS‐based phenotyping provides a promising and powerful tool for high throughput peanut breeding programs.

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Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning

Bagherian,

Bidese‐Puhl,

Bao

et al. 2023

The Plant Phenome Journal

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Agronomic and physiological traits in peanut (Arachis hypogaea) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL (R2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models (R2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits (R2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model (R2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut.

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

Cited by 56 publications

References 11 publications

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Utility of unmanned aerial systems for measuring plant height and plant vigor among several Georgia runner peanut cultivars

Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning

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