UAV imaging with low-cost multispectral imaging system for precision agriculture applications

Honrado, Jaime Luis E.; Solpico, Dominic B.; Favila, C. M.; Tongson, Eden; Tangonan, Gregory L.; Libatique, Nathaniel Joseph C.

doi:10.1109/ghtc.2017.8239328

Cited by 29 publications

(15 citation statements)

References 15 publications

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“…The latter provides crop-scale images with limited resolution to perform noninvasive image-based crop data estimation. In this regard, Unmanned Aerial Vehicles (UAVs) are the new technological step for crop monitoring [11,19,2]. In the last decade, UAVs have been key to solve di↵erent problems in agriculture that mostly required high-precision crop data, e.g., crop parcels detection [10], fruit detection [4], crop yield improvement [21], crop variable measurement [15,14], and crop terrain mapping [8,13].…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery

Devia

Rojas

Petro

et al. 2019

J Intell Robot Syst

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High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.

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Section: Introductionmentioning

confidence: 99%

High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery

Devia

Rojas

Petro

et al. 2019

J Intell Robot Syst

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“…Estas imágenes pueden ser tomadas con satélites, vehículos aéreos tripulados, y, más recientemente, con vehículos aéreos no tripulados. Estos últimos suelen ser preferidos sobre los demás debido a que permiten obtener imágenes de alta resolución a bajo costo (Honrado et al, 2017).…”

Section: Revisión De Literaturaunclassified

Construcción de prototipo de CANSAT para toma de imágenes aéreas para detección de zonas de vegetación en agricultura de precisión

González

Gil

Serna

et al. 2021

Ciencia y Poder Aéreo

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En este artículo se presenta el diseño del CANSAT Heliospectrum[1] desarrollado para su aplicación en percepción remota y análisis de imágenes para competir en el concurso anual realizado por la Sociedad de Sistemas Electrónicos y Aeroespaciales (AESS), capítulo Colombia. El equipo Helios, desarrollador del CANSAT, hace parte del grupo de investigación ASTRA de la Universidad de Antioquia. El diseño se realizó siguiendo las restricciones impuestas por el concurso, en las cuales el picosatélite debía asemejarse a una radiosonda con transmisores, componentes electrónicos, sensores con medición de aceleraciones, presión atmosférica, temperatura, campos magnéticos y aptitud para volar a 1.000 metros de altura (como mínimo), caer libremente y aterrizar con la ayuda de un paracaídas. Así mismo, cumple con los requerimientos necesarios para hacer del CANSAT una plataforma multipropósito con énfasis en percepción remota para aplicación en agricultura de precisión. Se implementó una cámara RGB como carga útil que, con ayuda de un algoritmo de análisis de imágenes que implementa el Excess Green Index (ExG) y el espacio de color Hue, Saturation, Value (HSV), permitió obtener resultados cualitativos de índices de vegetación. El CANSAT está conceptualizado en cinco subsistemas: computadora de vuelo, telemetría, estructura, descenso y recuperación, y potencia. En la aviónica del picosatélite se encuentran componentes principales como el microcontrolador Teensy 3,5, GPS GY-NEO6MV2, IMU GY-89 y en el sistema de recuperación un paracaídas expulsado con un servomotor. El prototipo de CANSAT fue fabricado y probado con éxito por el equipo, resultando ganador de la categoría “Cóndores” de la competencia CANSAT Colombia de la AESS. [1] CANSAT con designación HELI0513 en el concurso CANSAT Colombia 2020.

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“…Crop monitoring and vegetation index calculation have been implemented using spectral imaging [41], where the authors talk about an RGB color imaging plus NIR imaging system that assess the scene for evaluating the health status of crops. [42] and [43] employ a multispectral imaging system on UAVs. [44] summarize the use of a multispectral sensor in the quality assessment of plant foods.…”

Section: Related Workmentioning

confidence: 99%

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

Raju

Sazonov

2020

IEEE Access

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There is a need for non-invasive methods for nutritional analysis of food that can address the drawbacks of current methods such as color photography, which cannot distinguish between energydense and zero calorie foods. This paper discusses a novel, multispectral approach for the identification of oil, particularly in salads, and defines a pseudo-reflectance term. A custom-made multispectral camera was used to collect a novel, publicly shared dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ∈[380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light. Mean spectra were extracted from the regions of interest in the multispectral cube and used to compute pseudo-reflectance. ANOVA (Analysis of Variance) was performed to look for variances in the pseudo-reflectance curves. ANOVA proved that the differences between group means of the four treatment groups (oil, vinegar, oil plus vinegar, control) were statistically significant. Pairs of groups showing the greatest significance were established using a Tukey post hoc test. Sequential Forward Selection (SFS) was used to determine 5 optimal feature wavelengths from the whole feature space (410 nm, 455 nm, 485 nm, 810 nm, and 850 nm). A combination of visible (VIS) and infrared (IR) wavelengths, selected using SFS, showed the greatest potential for discrimination between groups containing oil and groups that do not contain oil with a classification accuracy of 84.20%. The pseudo-reflectance values were statistically proven to be sensitive to the presence of oil as a dressing. This research has demonstrated the feasibility of implementing a multispectral imaging technique for identifying the presence of oil in salads and possibly an energy content detection system. INDEX TERMS Multispectral imaging, energy density, food detection, high calorie detection, dietary assessment.

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UAV imaging with low-cost multispectral imaging system for precision agriculture applications

Cited by 29 publications

References 15 publications

High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery

High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery

Construcción de prototipo de CANSAT para toma de imágenes aéreas para detección de zonas de vegetación en agricultura de precisión

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

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