Image processing analysis to track colour changes on apple and correlate to moisture content in drying stages

Bora, Ganesh C.; Pathak, Rohit; Ahmadi, Mojtaba; Mistry, Purbasha

doi:10.1093/fqsafe/fyy003

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…Classification success indicators were obtained 93.02% (LR), 92.86% (MLP), 92.83% (SVM), 92.49% (DT), 92.39% (RF), 91.71% (NB), 88.58% (k-NN). When we look at the results of a successful acquisition, we can say that the study was successful [11].…”

Section: Litrature Reviewmentioning

confidence: 99%

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

2020

CEIS

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Rice is the source of Pakistan agriculture industry and food. For agriculture, industry and the oldest sector in the world use rice for different purpose. There are many challenges in the particular sector such as their analysis. This analysis mostly often related to its texture, color, shape, grain etc. In this study, Vision system used to check the quality of rice using some texture features such as color, shape and characteristics. In this study Computer Vision Image Processing tool applied on three different types of rice. Using this tool we apply pattern classification using nearest neighbor and K-nearest neighbor algorithm. Using these algorithms we get results of three varieties of rice Bastmati, Jasmine and White rice. In both algorithms white rice result show best from Basmati rice and Jasmine rice. White rice result is 93.75 % which is best as quality wise. Other tool also available like as MATLAB, Mazda etc for future more best prediction.

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Section: Litrature Reviewmentioning

confidence: 99%

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

2020

CEIS

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“…In image processing, the computer imaging technique has been employed to create, edit, and display graphical images, characters, and objects. The computer image analysis technique is a broad field which consists of computer domains and applications in food quality evaluation [10,11], grading and the sorting of agricultural products [12,13], as well as harvesting the crops [14], and estimating moisture content in the drying stage for the storability of the food product [15]. Computer imaging contributes to the development of digital agriculture.…”

Section: Classification Of Photonics Systems In Agriculturementioning

confidence: 99%

Applications of Photonics in Agriculture Sector: A Review

et al. 2019

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The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

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“…Imaging technique, which is nondestructive and convenient to operate, provides a new possibility for agricultural product quality evaluation. Among these imaging techniques, computer vision is widely used, especially in moisture and color detection [6][7][8]. However, computer vision only obtains the two-dimensional information of samples in visible bands.…”

Section: Introductionmentioning

confidence: 99%

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

Xiao

Bai

2020

Foods

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Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2c, R2p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2c, R2p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.

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Image processing analysis to track colour changes on apple and correlate to moisture content in drying stages

Cited by 17 publications

References 14 publications

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

Applications of Photonics in Agriculture Sector: A Review

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

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