A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing

Ireri, David; Belal, Eisa; Okinda, Cedric; Makange, Nelson Richard; Changying, Ji

doi:10.1016/j.aiia.2019.06.001

Cited by 115 publications

(63 citation statements)

References 20 publications

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“…1a. The spectral data of apple samples were collected in the reflectance mode by an Antaris II FI-NIR spectrometer (Thermo Electron Co., USA) which equipped with an InGaAs detector with high sensitivity, an integrating sphere, and a tungsten lamp (20W) [18]. The detector covered the spectral range from 10,000 to 4000cm −1 (1000-2500nm), and the spectral resolution of this spectrometer is 1.928cm −1 .…”

Section: B Spectra Acquisitionmentioning

confidence: 99%

Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy

Tian

Zhang

et al. 2019

IEEE Access

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Apple is one of the most popular fresh fruits with an extensive scope of regions owing to its nutrition and sweet in flavor. There is a large difference in the composition of the fruits growing in varying regions because of the variation in the growing regions, such as temperature, soil nutrients, etc. As a result, it is of significance to decrease the impact of region variability on the measurement of soluble solids content (SSC) in apples. To lessen the impact of region variability and enhance the predictive ability of on the model, our manuscript compared the performance of the two multi-region prediction models for the estimation of SSC in apples from multiple geographical regions. One multi-region prediction model was developed by merging SSC values and spectral data of all samples from multiple regions. The other multiregion prediction model was built for the determination of SSC in combination with region discriminant, model search strategy, and single-region models. Support vector machine (SVM) was applied to establish the model for discriminating the apples from multiple geographical regions. It was found that the region discriminant model achieved great results, with the classification accuracy of 99.52%. By comparing and analyzing the two multi-region prediction models, the optimal multi-region prediction model was obtained. Finally, to decrease the irrelevant spectral information and reduce the computational cost, the multi-region SSC prediction model was optimized in combination with various spectral preprocessing methods (multiple scatter correction (MSC), standard normal variate (SNV), and first derivative (FD) correction) and variable selection methods (Monte Carlo uninformative variables elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), and random frog (RF)). The overall results denoted that it was more accurate to estimate SSC in apples from the different geographical regions by using the multi-region models based on the region discriminant model in combination with SNV preprocessing algorithm and MC-UVE variable selection algorithm, and the prediction accuracy preceded the single-region models.

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Section: B Spectra Acquisitionmentioning

confidence: 99%

Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy

Tian

Zhang

et al. 2019

IEEE Access

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“…At present, there are many methods, such as electronic nose (Mohammad‐Razdari, Ghasemi‐Varnamkhasti, Yoosefian, Izadi, & Siadat, 2019), biosensor (Caetano & Machado, 2008; Dzyadevych et al, 2004; Pauliukaite, Zhylyak, Citterio, & Spichiger‐Keller, 2006), dielectric spectroscopy (De los Reyes, Heredia, Fito, De los Reyes, & Andres, 2007), computer vision technology (Arjenaki, Moghaddam, & Motlagh, 2013; Ireri, Belal, Okinda, Makange, & Ji, 2019), near infrared spectroscopy (NIRS; Qin, Chao, & Kim, 2011; Tiwari, Slaughter, & Cantwell, 2013), hyperspectral/multispectral imaging (Cho et al, 2013; Lee et al, 2011; Mollazade, Omid, Tab, Kalaj, & Mohtasebi, 2018), X‐ray imaging (Romero‐Dávila & Miranda, 2004), magnetic resonance imaging (Milczarek, Saltveit, Garvey, & McCarthy, 2009), Raman imaging (Qin et al, 2011), for tomato quality evaluation. Among these techniques, near infrared (NIR) or visible–near infrared spectroscopy (Vis–NIRS) and computer vision technology are the most commonly used non‐destructive testing techniques.…”

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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“…However, the grading accuracy decreased as the number of grading categories increased. [18] Piedad, et al [19] applied three machine learning methods such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest are used for the tier based classification of banana. The four features including color and shape features of banana are used for the classification of banana.…”

mentioning

confidence: 99%

Analysis of Crop disease detection with SVM, KNN and Random forest classification

KALE¹

2021

ITII

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Due to an uneven climatic condition crops are being affected which leads to decrease in agriculture yield. It greatly affects global agricultural economy. However, the condition becomes more worse when diseases are identified in crops. Agriculture plays a vital role in every country’s economy. Thus, there is a need to identify the crop disease before it is visible on a crop so that disease can be avoided by using appropriate measures. The traditional way of identifying a crop disease is through observation by naked eyes. But as it requires large number of experts and continuous monitoring of crop it will be costly for large fields. Hence, an automatic system is required which can not only examine the crops to detect disease but also can classify the type of disease on crops. The proposed system determines disease from an input image. The input image has to go through following stages: Image Acquisition, Image pre-processing, Image segmentation, Feature Extraction, and Classification in order to determine diseased crop and accordingly provides remedy for that disease. Infected crop image is taken as input in Image Acquisition stage. In Image pre-processing stage noise is removed from the input image by applying gaussian blur filter and non-local means denoising algorithm. Also, the background of image is eliminated using Thresholding algorithm. To extract Region of Interest (ROI) i.e. infected region from input image K-means Clustering algorithm is used. Then color, texture and shape features are extracted from ROI and features are further send to the classification stage. Three different classification algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest are implemented for classification out of which Support Vector Machine Algorithm is found to be best in terms of accuracy. Hence, classification is carried out by using Multivariate Support Vector Machine algorithm which detect disease present in crop accurately. In this way, the proposed system detects a disease from the given input image.

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A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing

Cited by 115 publications

References 20 publications

Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy

Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy

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

Analysis of Crop disease detection with SVM, KNN and Random forest classification

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