This article studies the sterilization effects of high-voltage pulsed electric field (PEF) of technology on filamentous fungi. A cell dielectric model was proposed based on the physical structure of filamentous fungi. Basic theories of the electromagnetic field were comprehensively applied, and the multiphysics field simulation software COMSOL Multiphysics was used for more detailed study. The effects of PEF treatment parameters and microbial characteristic parameters on the resulting cell membrane and nuclear membrane changes were simulated and analyzed. The results showed significant effects on the transmembrane voltage of the cell membrane and nuclear membrane from the electric field intensity, pulse duration, cell membrane thickness, superposition effect of the pulses. However, the amount of hyphae had little effect, and the number of cell nuclei and the thickness of the cell walls had almost no effect on the transmembrane voltage of the cell membranes and the nuclear membranes. The results provide theoretical support for applying high-voltage PEFs to kill fungi in practical applications.

Rice quality directly affects the final rice yield. In order to achieve rapid, non-destructive testing of rice seeds, this paper combines the three-dimensional laser scanning technology and back propagation (BP) neural network algorithm to build a rice seeds identification platform. The information on rice seed surface is collected from four angles and processed using Geomagic Studio software. Based on the noise filtering, smoothing of the point cloud, vulnerability repair, and downsampling, the three-dimensional (3D) morphological characteristics of a rice seed surface, and the projection features of the main plane cross-section are obtained through the calculation of the features. The experiments were performed on five rice varieties, including Da Hua aromatic glutinous, Hong Shi Ⅰ, Tian You VIII, Xin Dao X, and Yu Jing VI. The resulting input vector consisted respectively of: (1) nine 3D morphological surface features, (2) nine projection features of the main cross-section plane of rice, and (3) all of the above features. The results showed that for an input vector consisting of nine surface 3D morphological features, the recognition rate of the five rice varieties was 95%, 96%, 87%, 93%, and 89%, respectively; for an input vector consisting of nine projection features of the main cross-section plane of rice seeds, the recognition rate was 96%, 96%, 90%, 92%, and 89%, respectively; and lastly, for an input vector consisting of all the features, the highest recognition rate of 96%, 97%, 91%, 94%, and 90%, respectively, was achieved. The analysis showed that rice varieties could be identified by using 3D laser scanning. Therefore, the proposed method can improve the accuracy of rice varieties identification. Keywords: rice seeds identification, BP neural network, three-dimensional laser scanning, features, point cloud DOI: 10.25165/j.ijabe.20191206.5044 Citation: Feng X B, He P J, Zhang H X, Yin W Q, Qian Y, Cao P, et al. Rice seeds identification based on back propagation neural network model. Int J Agric & Biol Eng, 2019; 12(6): 122–128.