Maturity detection of single maize seeds based on hyperspectral imaging and transfer learning

Abstract

Seed maturity is closely related to seed germination, seed storage and the yield of the next generation of crops and it is an important indicator of seed quality. Maize is an important food, feed, and industrial raw material crop in China. Hyperspectral imaging technology has the advantages of spectral technology and machine vision technology and it can obtain information inside and outside the seed without damaging it. On the basis of hyperspectral imaging technology, transfer learning algorithms were used in this study to explore the transfer feasibility of maize seed maturity models amongst different varieties. Three transfer tasks were designed between different varieties on the basis of the model established for the Xianyu 335 variety. Cross-variety detection models were constructed using the classical machine-learning algorithm partial least square-linear discriminant analysis (PLS-LDA), the transfer learning algorithm transfer component analysis (TCA), and manifold embedded distribution alignment (MEDA). Results showed that the PLS-DA direct transfer model had varying degrees of model failure in all three transfer tasks. However, the average recognition accuracies of the TCA migration model and the MEDA migration model were 89.3% and 91.2%, respectively. Both of the two Transfer learning models can effectively solve the problem of different data feature distributions amongst different varieties, making the model more universal. It solves the problem of transferring the single-grain maize seed maturity detection model amongst different varieties, providing effective, stable and universal technical support for maize seed detection, which is conducive to the promotion of single-grain sowing technology and the improvement of agricultural automation level.