Overcurrent-driven LEDs for consistent image colour and brightness in agricultural machine vision applications

Mirbod, Omeed; Choi, Daeun; Thomas, Roderick; He, Long

doi:10.1016/j.compag.2021.106266

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…This in turn led to the largest deviation of CDI values from what measured in dark conditions in both east and west sides of the canopies. Using their system, Mirbod et al [10] noticed that when the sun was in front of the camera, the auto-exposure settings of the LED illuminants attempted to adjust the image brightness using the sky regions and caused a darkening of the foreground canopies, but a similar issue was not encountered in our study. Recent research [11] highlighted a trend to develop camera systems that are illumination-invariant and attempt to overcome the influence of external light and different exposure on the collected images.…”

Section: Discussionmentioning

confidence: 67%

“…Afterwards, skin colour can be determined within the detection perimeter (e.g., box or circle) by defining the number of pixels for colour extraction. Accurate fruit colour measurements using proximal sensing techniques such as machine vision are strongly affected by external light [10,11]. In fact, external light such as direct or background sunlight can substantially alter the brightness and chromaticity of the pixels where fruit colour is measured, which in turn may cause a change in the most typical colour parameters used for stone fruit quality estimation (i.e., a* and hue angle).…”

Section: Introductionmentioning

confidence: 99%

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A Fruit Colour Development Index (CDI) to Support Harvest Time Decisions in Peach and Nectarine Orchards

et al. 2022

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Fruit skin colour is one of the most important visual fruit quality parameters driving consumer preferences. Proximal sensors such as machine vision cameras can be used to detect skin colour in fruit visible in collected images, but their accuracy in variable orchard light conditions remains a practical challenge. This work aimed to derive a new fruit skin colour attribute—namely a Colour Development Index (CDI), ranging from 0 to 1, that intuitively increases as fruit becomes redder—to assess colour development in peach and nectarine fruit skin. CDI measurements were generated from high-resolution images collected on both east and west sides of the canopies of three peach and one nectarine cultivars using the commercial mobile platform Cartographer (Green Atlas). Fruit colour (RGB values) was extracted from the central pixels of detected fruit and converted into a CDI. The repeatability of CDI measurements under different light environments was tested by scanning orchards at different times of the day. The effects of cultivar and canopy side on CDI were also determined. CDI data was related to the index of absorbance difference (IAD)—an index of chlorophyll degradation that was correlated with ethylene emission—and its response to time from harvest was modelled. The CDI was only significantly altered when measurements were taken in the middle of the morning or in the middle of the afternoon, when the presence of the sun in the image caused significant alteration of the image brightness. The CDI was tightly related to IAD, and CDI values plateaued (0.833 ± 0.009) at IAD ≤ 1.20 (climacteric onset) in ‘Majestic Pearl’ nectarine, suggesting that CDI thresholds show potential to be used for harvest time decisions and to support logistics. In order to obtain comparable CDI datasets to study colour development or forecast harvest time, it is recommended to scan peach and nectarine orchards at night, in the early morning, solar noon, or late afternoon. This study found that the CDI can serve as a standardised and objective skin colour index for peaches and nectarines.

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Section: Discussionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

A Fruit Colour Development Index (CDI) to Support Harvest Time Decisions in Peach and Nectarine Orchards

et al. 2022

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“…La agricultura inteligente presenta un aporte a esta categoría, mediante la aplicación de elementos de análisis de seguridad, la disponibilidad y precisión de datos necesarios para construir sistemas robustos y eficientes de seguimiento con sensores (Paul et al, 2022;Rettore de Araujo Zonella et al, 2020). Un avance significativo en este ámbito es la aplicación de "machine vision" como tecnología de captura de imágenes que están sujetas a variables de iluminación solar, desenfoque por movimiento, color y luz (Mirbod et al, 2021). Otro uso adicional de las imágenes soportadas por "machine vision", es la disponibilidad de estas como datos públicos para procesos de control de malezas, detección de frutas y otras aplicaciones con estándares para la creación de algoritmos de rendimiento en tareas de cultivo (Lu & Young, 2020).…”

Section: Digitalización Y Seguridad De La Informaciónunclassified

Trazabilidad en el sector agrícola: una revisión para el periodo 2017 – 2022

Zúñiga

2023

Agron. Mesoam.

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Introducción. La trazabilidad se considera en los sistemas empresariales como una herramienta de seguimiento y control enfocada en la medición y recolección de datos para la asignación eficiente de recursos. El sector agrícola no es ajeno a esta práctica porque al igual que otros sistemas industriales, integra necesidades de control a nivel de cultivo, abastecimiento de insumos, transformación, transporte y comercialización de productos. Objetivo. Identificar objetos y alcances de seguimiento, unidades de análisis y adopción de tendencias de trazabilidad en la cadena de suministro agrícola, para referenciar el desarrollo de estudios y publicaciones recientes que integran esta función de control en este sector. Desarrollo. La metodología aplicada se desarrolló a través de la búsqueda, selección y análisis de artículos en repositorios científicos como Science Direct y AGRIS, para identificar tendencias de trazabilidad agrícola en los años 2017 al 2022. Se reconocieron tendencias de aplicación e integración de los sistemas de trazabilidad en el sector agrícola entorno a distintos enfoques, entre ellos, la digitalización y seguridad de la información, la medición de la productividad agrícola y el impacto ambiental dentro del concepto de sostenibilidad. Se presentan en las conclusiones las líneas de investigación, así como las brechas de conocimiento para futuros trabajos. Conclusiones. Los resultados de la revisión en los últimos seis años enmarcan tendencias de trazabilidad en el seguimiento digital de procesos de cultivo, la medición de la productividad y el impacto ambiental. El grado de intervención directa en el productor representa la mayor proporción en la categoría del alcance logístico de trazabilidad. Por lo anterior, se recomienda a futuro el desarrollo de sistemas de trazabilidad que realicen seguimiento de indicadores de productividad, impacto ambiental y social de manera convergente, así como la participación integrada de actores del sector agrícola, entre ellos productores, asesores técnicos y entidades gubernamentales.

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“…The transmitter is composed of multiple super luminous 3.0x3.0mm LEDs (Luminus Devices, Inc.) of different wavelengths and the receiver utilizes a single photodiode (Thorlabs, Inc.) to obtain the return signals. The LEDs are individually run with separate drivers, triggered by a Teensy microcontroller to enable synchronized temporal modulation, the temporal modulation is further explored in references [5] and [6]. These LEDs are coaligned to a 30mm rail system by utilizing dichroic beam splitters (Thorlabs) that are organized to be permissive to the wavelengths passing through them.…”

Section: Ateam Transmissometermentioning

confidence: 99%

“…An issue that all transmissometers must contend with is background DC-level noise [1][2][3][4][5][6]. In most operating environments, solar radiation is the main contributor to background noise, but other artificial light sources may also contribute.…”

Section: Ateam Transmissometermentioning

confidence: 99%

Aerial transmissometer for EO atmospheric measurement

Lilledahl,

Laxton,

McMahan

et al. 2023

Laser Communication and Propagation Through the Atmosphere and Oceans XII

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This publication will depict ongoing efforts in development and ground based validation of an aerial transmissometer utilizing a ground control station composed of a collocated altazimuth mounted transmitter and receiver along with a gimbal mounted retroreflector operating on an Un-crewed Aerial System (UAS). The transmitter is composed of multiple super luminous LEDs of different wavelengths. The system measures bulk point-to-point transmission through the atmosphere and enables an investigation into atmospheric species due to wavelength dependent absorption. The measurements will be along dynamic propagation paths and enable the development of hemispherical ground truth datasets.

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Overcurrent-driven LEDs for consistent image colour and brightness in agricultural machine vision applications

Cited by 10 publications

References 15 publications

A Fruit Colour Development Index (CDI) to Support Harvest Time Decisions in Peach and Nectarine Orchards

A Fruit Colour Development Index (CDI) to Support Harvest Time Decisions in Peach and Nectarine Orchards

Trazabilidad en el sector agrícola: una revisión para el periodo 2017 – 2022

Aerial transmissometer for EO atmospheric measurement

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