Unsound wheat kernels are a key indicator for evaluating wheat quality.In this study,a comprehen-sive applicability evaluation of a machine vision-based rapid analyser was implemented for unsound wheat kernels.The parameters,including reliability,repeatability,stability,inter-table variation,and comparison with standard methods,were analysed for the development of a fully automated instrument for the detection of unsound wheat ker-nels.The results indicated that:the t-value of the reliability test was-0.14,the P-value was 0.887,greater than 0.05,and there was no significant difference between the measurement results of the instrument and the manual measurement results of the national standard method(GB/T 5494-2019),so the reliability of the instrument test passed the test;the P-value of the inverse contrast analysis of the repeatability was 1.000,greater than 0.05,the repeatability of the X2 values were all less than the X2 critical value of 11.07,the extreme deviation values were all less than the critical extreme deviation value of 0.71％,and the repeatability passed the test;the t-value of the ADF test statistic for stability was-6.48,smaller than the t-value of the case of a significance level of 1％-4.58,the X2 value of the stability data was 12.8,the critical value was 16.92,the critical value of the extreme deviation of the stability was 0.8％,the extreme difference of this data was 0.75％,and the stability passes the significance test;the t-value of the test result of the inter-station difference data was-0.92,and the P-value was 0.373,and the P-value was greater than 0.05,indicating that the original hypothesis that the difference between the two methods was zero and cannot be rejected under the significance level of 5％;the P-value of this instrumental determination compared with the manual method was 0.887,greater than 0.05,and it overcame the manual method.The P-value of this instrument was 0.887,greater than 0.05,and it overcame the shortcomings of the manual method,such as strong subjectivity,time-consuming and labour-intensive,and greatly improved the accuracy and timeliness.
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