Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence Dorée

Al-Saddik, Hania; Simon, Jean‐Claude; Brousse, Olivier; Cointault, Frédéric

doi:10.1017/s2040470017000802

Cited by 14 publications

(14 citation statements)

References 10 publications

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“…In one study looking at Huanglongbing (HLB) detection in citrus crops by Garcia-Ruiz and colleagues, it was found that the NIR-R index value and the average reflectance values at spectral bands of 560 nm and 710 nm were statistically different between healthy and HLB-infected trees [29]. Likewise, Flavescence dorée disease in vineyards was detected using a multi-spectral camera monitoring bands at approximately 530-600-650-690-730-750-800 nm, with an accuracy over 94% by Al-Saddik and colleagues [30]. In other studies, vegetation indices were more heavily used.…”

Section: Plant Stress Detectionmentioning

confidence: 97%

“…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%

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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: Plant Stress Detectionmentioning

confidence: 97%

Section: Multispectral and Nir Camerasmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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“…An RGB-Depth (RGB-D) camera, employed with two grayscale cameras (mvBlueFOX-MLC202bG), covering light sources with polarizing films and the multispectral sensors mounted to UAV platform, was used to monitor orange orchards for detecting citrus greening disease in Florida [50]. Al-Saddik et al [78] used multispectral sensors to differentiate Flavescence dorée diseased and healthy grapevines in a vineyard without using common vegetative indices. Several other studies also used multispectral cameras [36,48,70,[79][80][81][82][83].…”

Section: Multispectral Camerasmentioning

confidence: 99%

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

2021

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Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.

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“…Precision viticulture is an area of research that includes many applications, such as estimating growth [6], estimating evaporate-transpiration and harvest coefficients [7], vigor evaluation [8], water stress localization [9] or diseases detection [10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…So far, there is some researches on the different imaging systems for the VDD. Certain studies use images taken at the vine leaf level [10][11][12][13][14] which can be mounted in mobile robots. Other research is carried out on aerial images taken by drones at plot scale targeting the vine canopies [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information

Kerkech¹,

Hafiane²,

Canals³

et al. 2020

Lecture Notes in Computer Science

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Vine disease detection (VDD) is an important asset to predict a probable contagion of virus or fungi. Diseases that spreads through the vineyard has a huge economic impact, therefore it is considered as a challenge for viticulture. Automatic detection and mapping of vine disease in earlier stage can help to limit its impact and reduces the use of chemicals. This study deals with the problem of locating symptomatic areas in images from an unmanned aerial vehicle (UAV) using the visible and infrared domains. This paper, proposes a new method, based on segmentation by a convolutional neuron network SegNet and a depth map (DM), to delineate the asymptomatic regions in the vine canopy. The results obtained showed that SegNet combined with the depth information give better accuracy than a SegNet segmentation alone.

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Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence Dorée

Cited by 14 publications

References 10 publications

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information

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