Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables-A review

Chopde, Santosh; Patil, Madhav; Shaikh, Adil; Chavhan, Bahvesh; Deshmukh, Mahesh

doi:10.18805/ag.v38i02.7940

Cited by 6 publications

(3 citation statements)

References 41 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many image processing methods are easily affected by different lighting conditions. Therefore, the adjustment of the lighting system significantly affects the quality of the image and plays an important role in the overall efficiency and accuracy of many applications [22]. A well‐adjusted lighting condition can enhance the image quality, thereby improving image analysis and reducing blur and noise in the image.…”

Section: Methodsmentioning

confidence: 99%

Saury Sensing for Packaging

Zou

You

Kageyama

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Detecting damages on the body of the saury is important for packaging in the fisheries industry. Moreover, the appearance of a fish is important in fisheries. Fish damage detection relies on manual assembly lines, which are labor-intensive and time-consuming. This paper proposes a novel saury damage detection method that automatically localizes the saury body region, determines the head-tail orientation, and detects damaged parts from the body region. We collected a saury image dataset for training and built a simple naive convolutional neural network (CNN) model without transfer learning for damage discrimination. Seven evaluation metrics, such as accuracy, F1-score, Area under the ROC curve (AUC), and prediction time, were comprehensively considered to select the optimal parameter configuration including the number of layers, number of filters, filter size, and input image size. We also utilized the transfer learning of eight pretrained CNN models to classify the saury into two categories for damage discrimination. Inception-v3 and ResNet-101 achieved the highest accuracy of 98.2% for damage discrimination. Through extensive experiments and discussion of the results, this paper presents clear guidance for choosing pretrained networks for transfer learning in damage detection tasks.

show abstract

Section: Methodsmentioning

confidence: 99%

Saury Sensing for Packaging

Zou

You

Kageyama

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…High costs, tediousness and inconsistency associated with manual sorting and grading have been forcing the post harvest industry to apply automation in these postharvest operations. Chopde et al (2017) identified computer vision technology as an emerging technique in sorting and grading of fruits and vegetables that will improve productivity of industry and will also help to provide better quality produce to consumers. Kavdir and Guyer (2003) exploited the use of high-tech sensors or machine vision for apple grading and making the final grading decision using Fuzzy Logic, a computer based ap-S264 Eco.…”

Section: Application Of Ai In Fruit Gradingmentioning

confidence: 99%

Application of Artificial Intelligence in Horticulture: A Review

Sebastian,

Balakrishnan,

Koshy

et al. 2023

EEC

View full text Add to dashboard Cite

India, with its diverse soil and climate conditions and varied agro ecological regions, provides a possibility to grow many horticultural crops, which significantly contribute to the Indian economy by enhancing farm output, generating employment, and providing raw materials to various food-processing businesses. The amount of land allotted for horticulture is minimal, but the demand for the production of horticulture crops is high. Therefore, meeting the demand with minimum resources is a bit challenging, nevertheless can be achieved by the introduction of Artificial Intelligence (AI) to the field. Application of AI in horticultural crop production by predicting adverse climate conditions, providing suitable micro-environment through remote controlled drip irrigation, and its application in providing automated irrigation, determining crop genotype, disease diagnosis, pest and weed management, maturity identification of fruits and vegetables, yield forecasting, automated harvesting using robots, reducing post-harvest losses and its role in fruit quality assessment and in processing industry is elaborated in this review.

show abstract

“…Machine vision technology based on visible imaging system has become widely used for bruise detection in fruits over the past few years [7–9]. However, early changes in bruise area for fruit are difficult to be detected by traditional RGB vision system for part of fruits including cheery [10].…”

Section: Introductionmentioning

confidence: 99%

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis

Shao

Xuan

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry’s competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350–2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.

show abstract

Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables-A review

Cited by 6 publications

References 41 publications

Saury Sensing for Packaging

Saury Sensing for Packaging

Application of Artificial Intelligence in Horticulture: A Review

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis

Contact Info

Product

Resources

About