Method of fruits and vegetables storage shelf life prediction based on multi-source information fusion

Meicen Guo; Jiping Qiao; Qiuyuan Wu

doi:10.1117/12.2643458

9 October 2022 Method of fruits and vegetables storage shelf life prediction based on multi-source information fusion

Meicen Guo, Jiping Qiao, Qiuyuan Wu

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122461L (2022) https://doi.org/10.1117/12.2643458
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

In the post-harvest supply chain of fruits and vegetables, the spoilage rate during storage is as high as 10% due to improper management measures, which has seriously restricted the development of related industries. In response to this problem, this paper proposes a method for predicting the storage shelf life of fruits and vegetables. In this paper, we develops a micro-environment monitoring system based on the Internet of Things (LOT) technology, integrating the sensors of temperature and humidity, oxygen and carbon dioxide, using the LoRa wireless communication module to realize real-time data transmission; With the help of MATLAB platform, we build a multi-sensor data fusion model based on artificial neural network (ANN), and we convert the environmental parameters with multi-source coupling relationship into shelf life by this model. The results show that the model has good prediction performance and meets the requirements of shelf life prediction. The method can effectively reduce the economic losses caused by fruits and vegetables spoilage, when being applied to the post-harvest supply chain management of fruits and vegetables.

Citation Download Citation

Meicen Guo, Jiping Qiao, and Qiuyuan Wu "Method of fruits and vegetables storage shelf life prediction based on multi-source information fusion", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122461L (9 October 2022); https://doi.org/10.1117/12.2643458

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