Scientific Study

BACKGROUND: Shrivelled defect has a serious effect on walnut quality and is a common internal defect of in-shell walnuts. However, only depend on 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. So the golden-section was used to search the optimal discrimination threshold and the information integration of force sensing and machine vision was used to identificate the shrivelled walnut and 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 walnut and sound walnut, which also makes a basis for on-line detection of in-shell shrivelled walnut.