Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Peng Xu, Qian Tan, Yunpeng Zhang, Xiantao Zha, Songmei Yang and Ranbing Yang
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Peng Xu: College of Information and Communication Engineering, Hainan University, Haikou 570228, China
Qian Tan: College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
Yunpeng Zhang: College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
Xiantao Zha: College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
Songmei Yang: College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
Ranbing Yang: College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China

Agriculture, 2022, vol. 12, issue 2, 1-16

Abstract: Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

Keywords: machine vision; maize seeds; classification; deep learning; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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