Design of a tomato classifier based on machine vision

Liu, Li; Li, Zhengkun; Yu-fei, Lan; Shi, Yinggang; Cui, Yongjie

doi:10.1371/journal.pone.0219803

Cited by 32 publications

(18 citation statements)

References 12 publications

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“…Therefore, this paper also conducted a research on the relationship between photo number n and model accuracy, hoping to get satisfactory results with smaller n value. The volume values of tomato samples numbered 101~200 were measured again, due to the particularity of the wireframe model, n was set to n [5,15], and the data source of the BPNN came from tomato samples numbered 1~90 when n [1,15], and the obtained model accuracy is shown in Fig. 10.…”

Section: Influence Of Photo Number On the Prediction Results Of The Two Modelsmentioning

confidence: 99%

“…In China, approximately 60 million tons of tomatoes are produced each year. Since consumers often judge the quality of tomatoes by surface characteristics such as color, size, and shape [3][4], many experts and scholars have carried out relevant research on automatic tomato grading, and most of their studies focus on using machine vision to detect the ripeness, color, size, and surface defects of tomatoes [1,5]. Among them, the size of tomatoes is an important factor affecting consumers to buy tomatoes, and the volume of tomatoes is the most intuitive way to describe the size of tomatoes.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Tomato Volume Measurement Method based on Machine Vision

Sun

Liu

et al. 2021

Teh. vjesn.

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Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%.

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Section: Influence Of Photo Number On the Prediction Results Of The Two Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Tomato Volume Measurement Method based on Machine Vision

Sun

Liu

et al. 2021

Teh. vjesn.

View full text Add to dashboard Cite

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“…The author uses preprocessing, segmentation, feature extraction, and classification. Liu et al [21] proposed the computer vision-based tomato grading algorithm based on color features, size and shape of images. The features were extracted using the histograms of color HSV model and size of tomatoes using first-order first-difference (FD) shape description method.…”

Section: State-of-the-art Fruit and Vegetable Image Classification Mementioning

confidence: 99%

FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality

Das

Kar

Sekh

2021

Communications in Computer and Information Science

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Quality-based grouping or grading of fruits and vegetables played an important role in many stages of agricultural production. The method is used for sorting items based on the size, color, presence of damage etc. Manual grading is laborious and time consuming. Hence, not suitable for processing massive amounts. Grading different fruits and vegetables based on size or damages is popular in literature. More complex problems such as freshness based grading got less attention.Here, we introduce a new freshness grading dataset namely FGrade. The dataset is a collection of (∼6K) high-quality tomato images collected in a form of day-by-day degradation. The dataset is labelled in 2, 4, 6 and 10 classes using a structured manner. We have benchmarked the dataset using state-of-the-art image classification methods. We believe that the dataset is challenging and will attract computer vision researchers in the future. The dataset is available publicly (https://github.com/skarifahmed/FGrade).

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“…The accuracy of the sorting system is 96.47%. Liu et al [7] classified the tomato samples into color, size, and shape. The proposed approach performed color-based classification by observing the range of hue values.…”

Section: Introductionmentioning

confidence: 99%

An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet

Das¹,

Yadav²,

Yadav³

2021

ISI

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Tomato maturity classification is the process that classifies the tomatoes based on their maturity by its life cycle. It is green in color when it starts to grow; at its pre-ripening stage, it is Yellow, and when it is ripened, its color is Red. Thus, a tomato maturity classification task can be performed based on the color of tomatoes. Conventional skill-based methods cannot fulfill modern manufacturing management's precise selection criteria in the agriculture sector since they are time-consuming and have poor accuracy. The automatic feature extraction behavior of deep learning networks is most efficient in image classification and recognition tasks. Hence, this paper outlines an automated grading system for tomato maturity classification in terms of colors (Red, Green, Yellow) using the pre-trained network, namely 'AlexNet,' based on Transfer Learning. This study aims to formulate a low-cost solution with the best performance and accuracy for Tomato Maturity Grading. The results are gathered in terms of Accuracy, Loss curves, and confusion matrix. The results showed that the proposed model outperforms the other deep learning and the machine learning (ML) techniques used by researchers for tomato classification tasks in the last few years, obtaining 100% accuracy.

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Design of a tomato classifier based on machine vision

Cited by 32 publications

References 12 publications

A Novel Tomato Volume Measurement Method based on Machine Vision

A Novel Tomato Volume Measurement Method based on Machine Vision

FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality

An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet

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