Hyperspectral imaging system for disease scanning on banana plants

Ochoa, Daniel; Cevallos, Juan Cevallos; Vargas, Germán; Criollo, Ronald; Romero, Dennis; Castro, Rodrigo; Bayona, Oswaldo

doi:10.1117/12.2224242

Cited by 14 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We use the pushbroom scanner described in (Ochoa et al 2016). As shown in Figure 1(a), it consists of a high-resolution 12-bit monochrome CCD camera (B) with extended infrared sensitivity (1500 M-GE Thorlabs) attached to an spectrograph (Specim Inspector V10) with a spectral range from 364 nm to 1031 nm and nominal spectral resolution of 4.55 nm (C).…”

Section: Image Acquisitionmentioning

confidence: 99%

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Villegas

Liao

Criollo

et al. 2017

Geo-spatial Information Science

Self Cite

View full text Add to dashboard Cite

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods.

show abstract

Section: Image Acquisitionmentioning

confidence: 99%

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Villegas

Liao

Criollo

et al. 2017

Geo-spatial Information Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A number of articles devoted to the early detection of diseases in various crops should also be mentioned, if without detailed analysis. These articles describe the detection of diseases such as: red leaf blotch on almond caused by Polystigma amygdalinum [149]; black sigatoka on bananas caused by Mycosphaerella fijiensis [150,151]; barley blast caused by Magnaporthe oryzae [152]; grapevine leafstripe [153]; grapevine leafroll caused by Grapevine leafroll-associated virus 3 [154]; verticillium wilt of olive caused by Verticillium dahliae [155,156]; Xylella fastidiosa disease on olive trees [157]; peanut early leaf spot caused by Cercospora arachidicola S. Hori and late leaf spot caused by Cercosporidium personatum [158]; peanut bacterial wilt caused by Ralstonia solanacearum [159]; charcoal rot on soybean caused by Macrophomina phaseolina (Tassi) Goid [160] and corn leaf spot caused by Phaeosphaeria maydis (Henn.) [161].…”

Section: Hyperspectral Remote Sensing Of Other Crops and Their Diseasesmentioning

confidence: 99%

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Terentev

Долженко

Fedotov

et al. 2022

Sensors

126

View full text Add to dashboard Cite

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

show abstract

“…Rather than collecting spectra from an entire image or an entire plant leaf, where spectra from the stressed and unaffected areas are mixed together, hyperspectral imaging can provide more sophisticated data that can isolate spectra only from the affected area and identify specific imaging patterns and characteristics. This method has become increasingly popular for plant phenotyping and stress detection in agriculture [29][30][31] and has been used to identify plant responses to both abiotic and biotic stresses, such as drought stress in maize [32] and barley [33], yellow rust [34] and powdery mildew [35] in wheat, salt stress in okra [36], and Black Sigatoka disease in banana plants [37].…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Zubler

Yoon

2020

Biosensors

View full text Add to dashboard Cite

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

show abstract

Hyperspectral imaging system for disease scanning on banana plants

Cited by 14 publications

References 7 publications

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Contact Info

Product

Resources

About