Variety Identification of Coated Maize Seed Based on Deep Learning
In order to realize the low cost,efficient and convenient variety identification of coated maize seed,a dataset was constructed based on 23 100 double-sided images of 18 varieties and 4 colors of coated maize seeds collected by smartphone,and the lightweight convolutional neural network models ShuffleNetV2,MobileNetV3,MobileViT,MobileOne,RepGhostNet and the integrated models based on the above models were used to identify coated maize seed variety.The results showed that the identification accuracy and the comprehensive performance of the five single models were high.The identification accuracies were 98.48%,98.23%,98.44%,98.23%and 98.01%,respectively.The model sizes were 1.55,4.96,4.42,6.97 and 4.19 MB,respectively.The inference speeds were 106,94,84,212 and 94 f/s,respectively.The identification accuracy of the integrated models was higher than that of the single models,and the identification accuracy of the integrated model composed of ShuffleNetV2 and MobileViT was 99.22%.The analysis found that the false identification only occurred in the varieties of the same color coated seeds,and as the number of varieties of the same color coated seeds increased,the model's identification accuracy had a downward trend.
国家科技期刊平台
NSTL
万方数据
