Background: The shrivelled defect of walnuts has a serious effect on walnut quality and is a common internal defect of in-shell walnuts. However, only depending on a single detection technique such as machine vision to detect in-shell shrivelled walnuts is challenging. Meanwhile, the threshold has a great impact on the accuracy of the discrimination analysis. Therefore, the golden-section was used to search the optimal discrimination threshold and the information integration of force sensing and machine vision was used to identify the shrivelled walnut and the sound walnut.
Results: A discrimination model for in-shell shrivelled walnut based on information integration of force sensing and machine vision was built. The optimal threshold was determined as 0.3464 by the golden-section method, and the optimal threshold was used to discriminate the in-shell shrivelled walnut and the sound walnut. The discriminant accuracy of in-shell shrivelled and sound walnuts were 96.97% and 85.29%, respectively, and the total discriminant accuracy reached 93.00% based on the discrimination model.
Conclusion: The results indicated the information integration of force sensing and machine vision based on the golden-section search optimal discrimination threshold is a potential method to discriminate shrivelled walnuts and sound walnuts, which also makes a basis for on-line detection of in-shell shrivelled walnut. © 2019 Society of Chemical Industry.
Keywords: force sensing; golden-section search; in-shell shrivelled walnut; information integration; machine vision; non-destructive detection.
© 2019 Society of Chemical Industry.