Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the early detection of apple scab disease

Nouri, Maroua; Gorretta, Nathalie; Vaysse, P.; Giraud, Michel; Germain, Christian; Keresztes, Barna; Roger, Jean‐Michel

doi:10.1016/j.dib.2017.12.043

Cited by 26 publications

(16 citation statements)

References 4 publications

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“…The datasets in [ 35 , 36 ] consist of images of objects taken indoors. There are some datasets which consist of a specific category of objects, like paintings [ 42 ], leaves from apple tree [ 43 ], textiles [ 39 ], honey [ 41 ] and wood samples [ 44 ]. Most of the available datasets are sampled or re-sampled at 10 nm intervals, while some of them have different sampling intervals, e.g., [ 28 ] has

nm sampling step, while datasets in [ 41 , 43 ] have spectral sampling intervals of 6 nm and

nm, respectively.…”

Section: Comparison Of Hytexila With Existing Hyperspectral Datasementioning

confidence: 99%

“…The information in NIR is valuable for material classification [ 45 , 46 , 47 ], and the identification of textile fibers [ 48 ] and minerals [ 49 ]. Although some of the hyperspectral datasets provide information in the NIR region, they are either for specific samples, as in [ 41 , 42 , 43 ], or consist of outdoor scenes with many objects, as in [ 28 , 32 ]. Each image in our dataset consists of one specific, rather flat, object.…”

Section: Comparison Of Hytexila With Existing Hyperspectral Datasementioning

confidence: 99%

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HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Khan

Mihoubi

Mathon

et al. 2018

Sensors

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We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

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nm sampling step, while datasets in [ 41 , 43 ] have spectral sampling intervals of 6 nm and

nm, respectively.…”

Section: Comparison Of Hytexila With Existing Hyperspectral Datasementioning

confidence: 99%

Section: Comparison Of Hytexila With Existing Hyperspectral Datasementioning

confidence: 99%

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Khan

Mihoubi

Mathon

et al. 2018

Sensors

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“…This method produced 25 healthy and 25 infected spectra as shown in Figure . This data set is described in detail in Nouri et al…”

Section: Methodsmentioning

confidence: 99%

VSN: Variable sorting for normalization

Rabatel

Marini

Walczak

et al. 2019

Journal of Chemometrics

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Spectrometric and analytical techniques in general collect multivariate signals from chemical or biological materials by means of a specific measurement instrumentation, usually in order to characterize or classify them through the estimation of one of several compounds of interest. However, measurement conditions might induce various additive (baseline) or multiplicative effects on the collected signals, which may jeopardize the accuracy and generalizability of estimation models. A common way of dealing with such issues is signal normalization and in particular, when the baseline is constant, the standard normal variate (SNV) transform. Despite its efficiency, SNV has important drawbacks, in terms of physical interpretation and robustness of estimation models, because all the variables are equally considered, independently on what their actual relationship with the response(s) of interest is. In the present study, a novel algorithm is proposed, named variable sorting for normalization (VSN). This algorithm automatically produces, for a given set of multivariate signals, a weighting function favoring signal variables that are only impacted by additive and multiplicative effects, and not by the response(s) of interest.When introduced in SNV preprocessing, this weighting function significantly improves signal shape and model interpretation. Moreover, VSN can be successfully used not only for constant but also with more complex baselines, such as polynomial ones. Together with the description of the theory behind VSN, its application on various synthetic multivariate data, as well as on real SWIR spectral data, is presented and discussed.

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“…With the development of image processing technology and its application in the agricultural field, crop disease identification has improved [18], [19], [36]. Computer image processing refers to converting an image signal into a digital signal and then processing it by using a computer.…”

Section: Related Workmentioning

confidence: 99%

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

Fan

et al. 2020

IEEE Access

124

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Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC-ResNet) model for fine-grained disease identification in the system. MDFC-ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC-ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.

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Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the early detection of apple scab disease

Cited by 26 publications

References 4 publications

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

VSN: Variable sorting for normalization

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

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