Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy

Document Type : Research Article


1 sari agricultural sciences and natural resources university

2 Dep. of Biosystems Eng., Faculty of Agr. Eng. SANRU


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.

Graphical Abstract

Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy


  • Developing a non-destructive method to detect the percentage of devoid paddy based on machine vision
  • Using the color characteristics of individual pixels to detect the percentage of devoid paddy
  • The possibility of online use of the proposed system in moving paddy mass
  • Maximum detection error of less than 10% in mass product mode


Main Subjects

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