College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi, China.
J Sci Food Agric. 2019 Jun;99(8):3941-3949. doi: 10.1002/jsfa.9618. Epub 2019 Mar 18.
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.
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.
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.
核桃干瘪缺陷对核桃质量有严重影响，是带壳核桃常见的内部缺陷。然而，仅依靠机器视觉等单一检测技术来检测带壳干瘪核桃具有挑战性。同时，阈值对判别分析的准确性有很大影响。因此，采用黄金分割法搜索最佳判别阈值，并采用力学传感与机器视觉信息融合技术来识别干瘪核桃和完好核桃。
建立了基于力学传感与机器视觉信息融合的带壳核桃干瘪判别模型。采用黄金分割法确定最佳阈值为 0.3464，并用最佳阈值对带壳核桃的干瘪与完好进行判别。带壳核桃干瘪与完好的判别准确率分别为 96.97%和 85.29%，基于判别模型的总判别准确率达到 93.00%。
结果表明，基于黄金分割搜索最佳判别阈值的力学传感与机器视觉信息融合是判别干瘪核桃和完好核桃的一种有潜力的方法，为带壳核桃干瘪的在线检测奠定了基础。 © 2019 英国化学工程师学会。
