Non-destructive detection method for wheat freshness degree based on delayed luminescence

Accurate identification to freshness degree is one of the preconditions for secure storage of wheat. Up to now, the detection on wheat freshness mainly depends on biochemical methods. However, biochemical methods are always involved in a series of problems, such as long pretreatment processes to wheat samples, poor repeatability, destructive detection and so on, which hardly meet the detection requirements of scientific and intelligent grain storage. Therefore, finding out a simple, accurate, and non-destructive detection method for wheat freshness degree is of necessity. For this reason, we propose a novel wheat freshness degree detection model based on the delayed luminescence (DL) signals of wheat samples in this paper. Furthermore, a bidirectional LSTM network based on Walsh coding (Walsh-Bi-LSTM) is introduced in order to make the detection model have the errorcorrecting performance by reasonably splitting the multi-classification target task into several binary classification targets. Shown by the experimental results, the classification accuracy rates of the detection model established in this paper achieve 90% and 94% on the test sets of storage wheat and artificial aging seed wheat samples, improving 9% and 12% compared with conventional Bi-LSTM network model respectively, which validates that the proposed model can accurately detect wheat freshness degree.