Sensors in postharvest technologies: Evidence from patent portfolio analysis

The objective of the present study was to provide insight into trends of sensors in postharvest of fresh produce. Patent portfolio of 881 patents obtained through a multistep refinement of the PatSnap patent database was analyzed. Intensive patenting activities started in 2014 with most patents originating from Asia, with China as the leading patenting authority. Latent Dirichlet Allocation topic modelling differentiated existence of three distinct topics, out of which two are related to the application of sensors in (A) produce sorting and packaging along with related processes, and (B) produce storage, respectively. The third topic is related to the development and application of a diversity of sensors for analysis of the fresh produce and storage environment, as well as solutions for the application of sensors in monitoring, data collection and development of prediction models. The analysis of patent portfolio and trends highlights the ongoing and rapid advancement of sensing technologies in fresh produce postharvest, which has a substantial impact on the landscape of postharvest processes. Our findings indicate that these developments are poised to transform existing practices, particularly in quality and safety monitoring. Additionally, they are expected to provide comprehensive datasets that can support the broader integration of artificial intelligence into postharvest processes, ultimately leading to more coherent and efficient supply chains.

Automated summary

This study analyzes the patent portfolio and trends of sensors in postharvest of fresh produce, providing insights into the development of sensing technologies in this area. China is the leading country in patent applications, and the patent themes include produce sorting and packaging, produce storage, and the development and application of sensors. The findings highlight the significant impact of these technologies on postharvest processes, particularly in quality and safety monitoring. Moreover, they are expected to facilitate the integration of artificial intelligence into postharvest processes, enhancing the coherence and efficiency of supply chains.