Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy
The common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Iranian Research Organization for Science and Technology (IROST)
2023-07-01
|
| Series: | فناوریهای جدید در صنعت غذا |
| Subjects: | |
| Online Access: | https://jift.irost.ir/article_1328_0cdc79cebdcb5c89d5b2e82e639a48a4.pdf |
| _version_ | 1797326175823986688 |
|---|---|
| author | Fatemeh Sadeghi Davood Kalantari Sajad Kiani |
| author_facet | Fatemeh Sadeghi Davood Kalantari Sajad Kiani |
| author_sort | Fatemeh Sadeghi |
| collection | DOAJ |
| description | The common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict the percentage of devoid/healthy rough rice grains. Digital images of five varieties of paddy were prepared in three states: healthy, devoid, and mixed, in two states scattered and piled. After pre-processing and segmentation, 3 color features and 5 morphological features were extracted for each rice grain. Principal component analysis (PCA) method was then used in order to identify the most effective features in distinguishing devoid rough from healthy rice. In the next step, multilayer perceptron (MLP) algorithm based on the main components obtained by PCA method was used to create models for identifying and classifying the samples. Root Mean Square Error (RMSE), correlation coefficient (R2), specificity and sensitivity were used to evaluate the modeling capability and validation of each algorithm. The obtained results showed that the designed intelligent method can identify devoid rough rice seeds with acceptable accuracy in all cultivars (R2P>0.81, RMSEp<0.219, Sensitivity>0.8 & Specificity>0.98). Therefore, the machine vision system in combination with artificial neural networks can be used as an intelligent and fast method at the entrance of rice bleaching factories to evaluate the quality of harvested rough rice and predict the percentage of unhealthy rough rice. |
| first_indexed | 2024-03-08T06:19:45Z |
| format | Article |
| id | doaj.art-bf7a79d05ad441edb8a399931651f66d |
| institution | Directory Open Access Journal |
| issn | 2783-350X 2783-1760 |
| language | English |
| last_indexed | 2024-03-08T06:19:45Z |
| publishDate | 2023-07-01 |
| publisher | Iranian Research Organization for Science and Technology (IROST) |
| record_format | Article |
| series | فناوریهای جدید در صنعت غذا |
| spelling | doaj.art-bf7a79d05ad441edb8a399931651f66d2024-02-04T09:16:56ZengIranian Research Organization for Science and Technology (IROST)فناوریهای جدید در صنعت غذا2783-350X2783-17602023-07-0110433535710.22104/ift.2023.6268.21411328Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddyFatemeh Sadeghi0Davood Kalantari1Sajad Kiani2sari agricultural sciences and natural resources universityDep. of Biosystems Eng., Faculty of Agr. Eng. SANRUsari agricultural sciences and natural resources universityThe common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict the percentage of devoid/healthy rough rice grains. Digital images of five varieties of paddy were prepared in three states: healthy, devoid, and mixed, in two states scattered and piled. After pre-processing and segmentation, 3 color features and 5 morphological features were extracted for each rice grain. Principal component analysis (PCA) method was then used in order to identify the most effective features in distinguishing devoid rough from healthy rice. In the next step, multilayer perceptron (MLP) algorithm based on the main components obtained by PCA method was used to create models for identifying and classifying the samples. Root Mean Square Error (RMSE), correlation coefficient (R2), specificity and sensitivity were used to evaluate the modeling capability and validation of each algorithm. The obtained results showed that the designed intelligent method can identify devoid rough rice seeds with acceptable accuracy in all cultivars (R2P>0.81, RMSEp<0.219, Sensitivity>0.8 & Specificity>0.98). Therefore, the machine vision system in combination with artificial neural networks can be used as an intelligent and fast method at the entrance of rice bleaching factories to evaluate the quality of harvested rough rice and predict the percentage of unhealthy rough rice.https://jift.irost.ir/article_1328_0cdc79cebdcb5c89d5b2e82e639a48a4.pdfimage processingartificial neural networkmatlabnondestructive test |
| spellingShingle | Fatemeh Sadeghi Davood Kalantari Sajad Kiani Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy فناوریهای جدید در صنعت غذا image processing artificial neural network matlab nondestructive test |
| title | Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy |
| title_full | Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy |
| title_fullStr | Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy |
| title_full_unstemmed | Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy |
| title_short | Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy |
| title_sort | development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy |
| topic | image processing artificial neural network matlab nondestructive test |
| url | https://jift.irost.ir/article_1328_0cdc79cebdcb5c89d5b2e82e639a48a4.pdf |
| work_keys_str_mv | AT fatemehsadeghi developmentofanintelligentmachinevisionsystemforthepurposeofonlinequalitymeasurementofricepaddy AT davoodkalantari developmentofanintelligentmachinevisionsystemforthepurposeofonlinequalitymeasurementofricepaddy AT sajadkiani developmentofanintelligentmachinevisionsystemforthepurposeofonlinequalitymeasurementofricepaddy |