Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method

Li, Jiangbo; Zhang, Ruoyu; Li, Jingbin; Wang, Zheli; Zhang, Hai-Liang; Zhan, Baishao; Jiang, Yinglan

doi:10.1016/j.postharvbio.2019.110986

Cited by 40 publications

(21 citation statements)

References 28 publications

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“…It consists of the transformation of the raw signal into percent reflectance, threshold-based background segmentation, and normalized spectral transformation to remove scattering effects. The watershed algorithm is also a popular image segmentation method, which is often used for single tree crown delineation and kernel and leaf segmentation [92]. In addition, some of the methods that have been applied effectively to gray-scale, color, and multispectral images have been optimized purposefully and play key roles in hyperspectral image segmentation, such as Grabcut [93], the method of Otsu [94], and edge detection [95,96].…”

Section: Diseases Classification Is the Attempt To Identify And Label The Pathogens Affecting The Plant Simultaneouslymentioning

confidence: 99%

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

et al. 2020

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The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

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Section: Diseases Classification Is the Attempt To Identify And Label The Pathogens Affecting The Plant Simultaneouslymentioning

confidence: 99%

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

et al. 2020

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“…Rather than any specific information on the volatiles detected in the samples, E‐nose provides an overall estimate. Some research has also been carried out in the field of food microbial contamination, including peaches, 15 strawberry, 16 and orange 17 . Nevertheless, most studies involving the discrimination of fungi have tended to focus on different genera rather than those of the same genus.…”

Section: Introductionmentioning

confidence: 99%

Early identification of Aspergillus spp. contamination in milled rice by E‐nose combined with chemometrics

Wang

Chen

et al. 2021

J Sci Food Agric

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BACKGROUND Rice grains can be contaminated easily by certain fungi during storage and in the market chain, thus generating a risk for humans. Most classical methods for identifying and rectifying this problem are complex and time‐consuming for manufacturers and consumers. However, E‐nose technology provides analytical information in a non‐destructive and environmentally friendly manner. Two‐feature fusion data combined with chemometrics were employed for the determination of Aspergillus spp. contamination in milled rice. RESULTS Linear discriminant analysis (LDA) indicated that the efficiency of fusion signals (‘80th s values’ and ‘area values’) outperformed that of independent E‐nose signals. Linear discriminant analysis showed clear discrimination of fungal species in stored milled rice for four groups on day 2, and the discrimination accuracy reached 92.86% by using an extreme learning machine (ELM). Gas chromatography–mass spectrometry (GC–MS) analysis showed that the volatile compounds had close relationships with fungal species in rice. The quantification results of colony counts in milled rice showed that the monitoring models based on ELM and the genetic algorithm optimized support vector machine (GA‐SVM) (R2 = 0.924–0.983) achieved better performances than those based on partial least squares regression (PLSR) (R2 = 0.877–0.913). The ability of the E‐nose to monitor fungal infection at an early stage would help to prevent contaminated rice grains from entering the food chains. CONCLUSIONS The results indicated that an E‐nose coupled with ELM or GA‐SVM algorithm could be a useful tool for the rapid detection of fungal infection in milled rice, to prevent contaminated rice from entering the food chain. © 2021 Society of Chemical Industry

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“…Because hyperspectral image contain both spectral and spatial information of sample, it can not only reflect the external characteristics of sample, but also reflect the intrinsic chemical composition and physical structure of sample, so it can be used to evaluate the quality and safety attributes of agricultural products. HSI technique has been successfully applied for defect detection of fruit, such as apple bruising (Huang, Li, Wang, & Chen, 2015), apple decay (Li, Luo, Wang, & Fan, 2019) and citrus decay (Gómez‐Sanchis et al, 2008; Li et al, 2016; Li, Zhang, et al, 2019). Similar to detection of decayed tomatoes, these defects were also very difficult to be identified by conventional imaging and spectroscopy technologies.…”

Section: Introductionmentioning

confidence: 99%

Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image‐spectrum merging technique

Wang

Zhang

et al. 2021

J Food Process Engineering

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Effective detection of tomatoes with early decay still remains one of the major problems in the post-harvest processing. The feasibility of using visible and near infrared (Vis-NIR) hyperspectral reflectance imaging coupled with image-spectrum merging processing and analysis technology to detect early decay on tomatoes was observed. Mean normalization method was used to correct the uneven illumination caused by the large curvature change and the reflective characteristics of tomato surface. Principal component (PC) clustering analysis of spectra was used to find the optimal PC that was then used to distinguish sound and decayed tissues of tomatoes. Based on the selected PC, three characteristic wavelengths images at 596, 666, and 858 nm were obtained and the new combination image was calculated. The pseudo-color image processing and RGB (red, green, and blue) transformation were applied to enhance the contrast between decayed and sound regions on the combined image. G component of RGB image and the corrected single-wavelength image at 666 nm were used for segmentation of the decayed region and stem-end on tomatoes, respectively. All samples including 120 decayed and 120 sound tomatoes were used

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Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method

Cited by 40 publications

References 28 publications

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

Early identification of Aspergillus spp. contamination in milled rice by E‐nose combined with chemometrics

Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image‐spectrum merging technique

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