Detection of rice seeds vigor based on photoacoustic spectrum combined with TCA transfer learning

Abstract: Seed vigor is one of the most important factors in determining rice yield. The nondestructive test methods of rice seeds vigor, such as near infrared spectroscopy and hyperspectral spectroscopy, are easily affected by seed skin color, particularly where the current models are difficult to adapt to new varieties of rice. In this study, a non-destructive testing method was proposed to detect the seed vigor of new varieties of rice, using the photoacoustic spectroscopy technology, combined with the transfer learning. First, six typical regional representative rice varieties were selected in different latitudes in China, including Yliangyou, Longjing, Nanjing, Ningjing, Wuyunjing and Xinliangyou county. An aging box model RXZ-128A was used for the artificially ageing under high temperature and humidity. The temperature in the aging box was 45°C, and the relative humidity was maintained at 95%, to obtain rice seeds with the aging time of 0, 1, 2, 3, 4, 5, 6, and 7 days. A Nicolet Is50R infrared spectrometer (Thermal Fish, USA) was used in conjunction with the PA300 photoacoustic cell produced by MTEC Photoacoustics to establish a rice seed photoacoustic spectrum acquisition system, thereby to acquire 8 different depths of rice seed photoacoustic spectrum information. The germination test was conducted on rice seeds with different aging days, and the average germination rates of 0-7 days aging days were 93.54%, 91.56%, 89.56%, 87.71%, 84.35%, 78.22%, 72.21%, and 66.33% respectively. After pre-processing and ensemble empirical mode decomposition denoising, the principal component analysis and competitive adaptive reweighted sampling can be used to reduce the dimension of spectrum, and thereby obtained the characteristic spectrum. Then, the Partial Least Squares Regression(PLSR), Back Propagation Neural Network(BP), Generalized Regression Neural Network(GRNN), Support Vector Regression(SVR), Convolutional Netural Network(CNN) prediction models of rice seed vigor were established for Yliangyou, Longjing, Nanjing, Ningjing, and Wuyunjing county, and the optimal modulation frequency were selected. Finally, a new CNN prediction model was established for a new rice seed vigor using source domain data, concurrently, the photoacoustic spectroscopy target domain data of the Xinliangyou rice seed was input after transfer learning into the newly established CNN model for vigor prediction. The germination test showed that with the deepening aging of rice seeds, the vigor, germination rate, and germination potential of rice seeds gradually decreased, the plant height of seedlings decreased, the dry weight decreased, and the seedlings became thin and grow slowly. The modeling results showed that the best scanning frequency of photoacoustic spectrum was 300 Hz, competitive adaptive reweighted sampling had good spectral dimension reduction effect, where the prediction accuracy of CNN model was higher, the correlation coefficient and root mean square error were better than 0.990 9 and 0.507 7, respectively. After transfer learning, the vitality of new rice varieties can be directly and accurately predicted only by training the data of source domain. In TCA transfer learning, the correlation coefficient of prediction for the Xinliangyou rice seed vigor increased from 0.718 5 to 0.990 3. The usage of photoacoustic spectroscopy deep scanning technology can be proved to be feasible to detect the vigor of different types of rice with high precision. After transfer learning, only a small amount of information about new varieties of rice are required to be used to accurately predict rice seed vigor.