Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

Lowe, Amy; Harrison, Nicola; French, A. P.

doi:10.1186/s13007-017-0233-z

Cited by 449 publications

(322 citation statements)

References 44 publications

Supporting

Mentioning

256

Contrasting

Unclassified

Order By: Relevance

“…Hyperspectral imaging uses wavelengths across the electromagnetic spectrum to generate indices such as normalized difference vegetative index to measure canopy coverage (Christopher et al ., ), as well as to calculate models for specific biochemical and physiological traits (Silva‐Perez et al ., ). Hyperspectral imaging can also be used to detect and classify disease and stress symptoms; however, collecting appropriate data and developing analysis pipeline can be a challenge within commercial breeding programmes (Lowe et al ., ). At CIMMYT large‐scale phenotyping for physiological traits has been incorporated into breeding programmes using both high‐throughput imaging and conventional phenotyping methods (Reynolds and Langridge, ).…”

Section: Developments In Phenotypingmentioning

confidence: 97%

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

2018

View full text Add to dashboard Cite

Summary Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in‐field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome‐level assembly of the wheat genome now facilitates a step‐change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene‐editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.

show abstract

Section: Developments In Phenotypingmentioning

confidence: 97%

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

2018

View full text Add to dashboard Cite

show abstract

“…HSI has been successfully used for detecting plant stresses caused by toxic metals, salt, diseases and pests . With its ability to acquire abundant spectral information and imaging details of plants, HSI exhibits promising potential in capturing the plant response of abiotic and biotic stresses at both leaf and canopy levels …”

Section: Introductionmentioning

confidence: 99%

Assessing crop damage from dicamba on non‐dicamba‐tolerant soybean by hyperspectral imaging through machine learning

Zhang

Huang

Reddy

et al. 2019

Pest Management Science

View full text Add to dashboard Cite

BACKGROUND Dicamba effectively controls several broadleaf weeds. The off‐target drift of dicamba spray or vapor drift can cause severe injury to susceptible crops, including non‐dicamba‐tolerant crops. In a field experiment, advanced hyperspectral imaging (HSI) was used to study the spectral response of soybean plants to different dicamba rates, and appropriate spectral features and models for assessing the crop damage from dicamba were developed. RESULTS In an experiment with six different dicamba rates, an ordinal spectral variation pattern was observed at both 1 week after treatment (WAT) and 3 WAT. The soybean receiving a dicamba rate ≥0.2X exhibited unrecoverable damage. Two recoverability spectral indices (HDRI and HDNI) were developed based on three optimal wavebands. Based on the Jeffries–Matusita distance metric, Spearman correlation analysis and independent t‐test for sensitivity to dicamba spray rates, a number of wavebands and classic spectral features were extracted. The models for quantifying dicamba spray levels were established using the machine learning algorithms of naive Bayes, random forest and support vector machine. CONCLUSIONS The spectral response of soybean injury caused by dicamba sprays can be clearly captured by HSI. The recoverability spectral indices developed were able to accurately differentiate the recoverable and unrecoverable damage, with an overall accuracy (OA) higher than 90%. The optimal spectral feature sets were identified for characterizing dicamba spray rates under recoverable and unrecoverable situations. The spectral features plus plant height can yield relatively high accuracy under the recoverable situation (OA = 94%). These results can be of practical importance in weed management. © 2019 Society of Chemical Industry

show abstract

“…From a different perspective, hyperspectral images have shown great potential in computer vision than RGB images, especially for the application that requires fine analysis of the spectral responses of object. Spectral data can used to vegetation monitoring (Glenn et al, 2012) (Aasen et al, 2015)tect environmental stress or plant diseases (Behmann et al, 2016) (Lowe et al, 2017) (Thomas et al, 2018).…”

Section: Instructionmentioning

confidence: 99%

3d Reconstruction From Uav-Based Hyperspectral Images

Liu

Peng

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Reconstructing the 3D profile from a set of UAV-based images can obtain hyperspectral information, as well as the 3D coordinate of any point on the profile. Our images are captured from the Cubert UHD185 (UHD) hyperspectral camera, which is a new type of high-speed onboard imaging spectrometer. And it can get both hyperspectral image and panchromatic image simultaneously. The panchromatic image have a higher spatial resolution than hyperspectral image, but each hyperspectral image provides considerable information on the spatial spectral distribution of the object. Thus there is an opportunity to derive a high quality 3D point cloud from panchromatic image and considerable spectral information from hyperspectral image. The purpose of this paper is to introduce our processing chain that derives a database which can provide hyperspectral information and 3D position of each point. First, We adopt a free and open-source software, Visual SFM which is based on structure from motion (SFM) algorithm, to recover 3D point cloud from panchromatic image. And then get spectral information of each point from hyperspectral image by a self-developed program written in MATLAB. The production can be used to support further research and applications. INSTRUCTIONHyperspectral images can be used for finding objects, identifying materials, or detecting spatial processes. Compared to RGB images, which only contain three bands of the visual part of the electromagnetic spectrum (red, green and blue light). The hyperspectral image contains continuous hundreds bands of the electromagnetic spectrum in the sensor's wavelength range. And it can provide considerable information on the spatial spectral distribution of the object.Traditionally, Hyperspectral images are acquired with satellites(Clark, 2016), airborne sensors (Quemada et al., 2014)or field spectrometers (Numata et al., 2008). Recently, with the size and weight of hyperspectral sensors have been shrinking and have been carried onboard of UAVs. Such as a new type of hyperspectral cameras like the UHD has been used for UAV remote sensing.In computer vision, 3D reconstruction is the process of recovering the shape and profile of real objects. This process can be accomplished either by active or passive methods. Active methods derive the depth map using methods of structure light, laser range finder and radiometric rangefinders, then reconstruct the 3D profile by numerical approximation approach and establish the object in the scene based on model. Although this method can get high quality data, it also requires expensive equipment. In contrast, the development of passive methods provides the opportunity for very low-cost three-dimensional data acquisition. Passive methods measure the radiance reflected or emitted by the object's surface to reconstruct the 3D profile from a set of digital images. Passive methods include structure from motion (SFM), photometric stereo, shape-from-shading etc.The SFM techniques can be used for estimating 3D structure from a series of overlappin...

show abstract

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

Cited by 449 publications

References 44 publications

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Assessing crop damage from dicamba on non‐dicamba‐tolerant soybean by hyperspectral imaging through machine learning

3d Reconstruction From Uav-Based Hyperspectral Images

Contact Info

Product

Resources

About