Determining the Authenticity of Information Uploaded by Blockchain Based on Neural Networks—For Seed Traceability

Kenan Zhao, Meng Zhang, Xiaofei Fan, Bo Peng, Huanyue Wang, Dongfang Zhang, Dongxiao Li and Xuesong Suo ()
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Kenan Zhao: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Meng Zhang: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Xiaofei Fan: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Bo Peng: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Huanyue Wang: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Dongfang Zhang: College of Horticulture, Hebei Agricultural University, Baoding 071000, China
Dongxiao Li: College of Agronomy, Hebei Agricultural University, Baoding 071000, China
Xuesong Suo: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China

Agriculture, 2025, vol. 15, issue 15, 1-21

Abstract: Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems.

Keywords: seed traceability; blockchain; neural networks; authenticity (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: 2025
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