Apple Binocular Visual Identification and Positioning System

Liu, Li; Xin, Qiao; Shi, Xindong; Wang, Yong; Shi, Yinggang

doi:10.18280/ria.330208

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Based on the Vision Assistant of LabVIEW, a basic vision processing program [13][14][15][16] was compiled to detect and extract tomato contours from the images. The basic processes include image segmentation, image graying, image binarization, particle filtering, and the IMAQ particle analysis for solving pixel area.…”

Section: Image Processingmentioning

confidence: 99%

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: Image Processingmentioning

confidence: 99%

A Novel Tomato Volume Measurement Method based on Machine Vision

Sun

Liu

et al. 2021

Teh. vjesn.

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“…The presentation [15] deals with the use of fuzzy cmeans clustering to build a fuzzy greenhouse climate model is compared against an artificial neural network (ANN) model and an adaptive neuro-fuzzy inference system (ANFIS) model. The authors use the adaptive neuro-fuzzy inference system to present a representation of the greenhouse design and control in the study [16].…”

Section: Introductionmentioning

confidence: 99%

Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique

Lachouri¹,

Mansouri²,

Lafifi³

2022

RIA

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The greenhouse climate is a non-linear system that contains multiple inputs (predictors) and multiple outputs (responses). This project aimed to provide a solution, aided by artificial intelligence, to the issue of variations in time, input and output factors in a greenhouse internal climate that can adversely affect tomato seedlings. Machine learning Methodologies such as fuzzy inference and neural networks have been applied to mimic idealistic behavior. This paper proposes an adaptive system based on artificial neural networks technique embedded with fuzzy logic technique calls Adaptive Neuro Fuzzy Inference System (ANFIS) to predict air humidity, air temperature, internal radiation, and CO2 concentration while the seeds grow, in order to produce favorable greenhouse climate conditions. The input parameters include ten meteorological and control actuators that majorly influence tomato plants during their growth process in the greenhouse climate. This discussion revolves around a linguistic ANFIS model that will operate during the 48 days that it takes for the seedlings to grow. It will provide estimates of the greenhouse climate using meteorological data along with control actuators rooted in trained neural networks with back propagation optimization algorithm, and 500 iterations of the least square algorithm. Simulations have revealed the efficiency of this model.

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“…Therefore, the nondestructive identification of apple varieties has become a prerequisite for apple industrialization and increased international competitiveness. Apple nondestructive testing (NDT) technology based on visible light technology has the characteristics of being fast and accurate [2][3][4][5][6][7][8][9][10][11]. It has developed very quickly in recent years and has been widely recognized in industry.…”

Section: Introductionmentioning

confidence: 99%

“…Use the ADMM to find the minimum value of the objective function(1) Input: the k-th sample xi of the i-th class, multiplier β, cluster center matrix D, μmax=103 , parameters λ1 and λ2. Output: group sparse representation coefficient gi 1.…”

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confidence: 99%

Apple Variety Recognition Based on Multiview Feature Fusion

Cai¹,

Li²,

Liu³

et al. 2020

RIA

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Nondestructive testing in apple variety identification has become a necessary prerequisite for the industrialization of apple production and increased international competitiveness. According to the characteristics of apple images, this paper designs and implements an apple variety identification algorithm (ACRMV) based on multiview technology. The method comprises two main steps, namely, discriminatory image block selection and a multiview classification algorithm. In the phase of image block selection, local features that occur frequently in one category but seldom in other categories are selected. In the multiview classification stage, a robust multiview classification fusion algorithm is designed based on image block features generated by different descriptors for each view. The experimental results show that ACRMV with the strategy of multifeature fusion and joint training is superior to its corresponding single-view method and to other multiview methods. The discriminative image block selection algorithm uses image blocks with greater discrimination as training data to reduce the influence of redundant data. The proposed method makes full use of the consistency and complementarity among different views to achieve the purpose of merging multiple views and jointly improving recognition performance.

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Apple Binocular Visual Identification and Positioning System

Cited by 3 publications

References 17 publications

A Novel Tomato Volume Measurement Method based on Machine Vision

A Novel Tomato Volume Measurement Method based on Machine Vision

Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique

Apple Variety Recognition Based on Multiview Feature Fusion

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