Abstract
Rice seeds’ infection with rice blast will directly lead to rice yield reduction or even crop failure in the next year. Therefore, it is very important accurately identify infected rice seeds. In this study, deep learning and hyperspectral imaging techniques were used for that purpose. First, hyperspectral image data were collected. Then, the UeAMNet (unsupervised extraction attention-based mixed CNN) model—designed in this study—was used to analyze these data and the results compared with the 2DCNN, 3DCNN, A2DCNN, A3DCNN, Ue2DCNN, Ue3DCNN, UeA2DCNN, UeA3DCNN, MNet, AMNet and UeMNet models using different training set (Tr) sizes. The results showed that the new UeAMNet model was superior to the comparison models when using different Tr sizes, and the accuracy could reach 100%. Notably, when Tr was only 0.05, the accuracy of this model still reached 96.85%. This showed that the proposed method could successfully identify infected rice seeds. Therefore, this study provides an approach for rice germplasm management and also for the development of crop disease identification methods in other parts of the world.
Published Version
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