测量水果特性的传感器技术专利组合分析的启示

IF 3.1 3区 农林科学 Q1 HORTICULTURE
Ž. Kevrešan, J. Mastilović, Dragan D. Kukolj, Dragana Ubiparip Samek, Renata Kovač, Marina Đerić, A. Bajić, G. Ostojić, S. Stankovski
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引用次数: 0

摘要

为了更好地了解用于测量水果特性及其变化的传感器的开发和应用趋势,我们生成并分析了以测量水果特性的传感器为重点的专利组合。形成了一个包含 189 项专利、实用新型和专利申请的专利组合。确定了三组专利:(i) 基于传感器的单个参数测量,(ii) 同时监测多个相关方面的多传感器解决方案,以及 (iii) 将传感器数据与人工智能工具和技术相结合的解决方案。对专利组合的分析指出了加强水果财产测量领域技术的主要驱动力。传感技术的发展使人们能够实时、快速、经济高效地测定越来越多、越来越复杂的水果特性和环境条件。将不同传感技术集成到多传感器系统中,用于监测水果质量、成熟度或新鲜度的整体解决方案,为采用新方法管理新鲜农产品开辟了道路。越来越多的解决方案将人工智能工具(如计算机视觉、机器学习和深度学习)应用到新鲜农产品供应链中,从而有可能在新鲜农产品管理的关键点上用基于证据的最佳解决方案取代人工决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insights from a Patent Portfolio Analysis on Sensor Technologies for Measuring Fruit Properties
A patent portfolio focusing on sensors for the measurement of fruit properties was generated and analyzed with the aim of contributing to a better understanding of the trends in the development and application of sensors intended for measuring fruit properties and their changes. A patent portfolio of 189 patents, utility models and patent applications was formed. Three groups of patents were identified: (i) sensor-based measurement of individual parameters, (ii) multisensor solutions for the simultaneous monitoring of multiple relevant aspects and (iii) solutions integrating sensor-derived data with artificial intelligence tools and techniques. The analysis of the patent portfolio pointed out the main driving forces of technology strengthening in the field of fruit property measurement. The development of sensing technologies enables the real-time, rapid and cost-effective determination of ever-increasing and more sophisticated sets of fruit properties and environmental conditions. Solutions integrating different sensing technologies into multisensor systems for monitoring fruit quality, ripening or freshness as holistic concepts opens avenues for the introduction of a new approach to fresh produce management. Increasing numbers of solutions introducing the application of artificial intelligence tools such as computer vision, machine learning and deep learning into the fresh produce supply chain contribute to the possibilities of substituting human decision-making at points of relevance for fresh produce management with optimal evidence-based solutions.
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来源期刊
Horticulturae
Horticulturae HORTICULTURE-
CiteScore
3.50
自引率
19.40%
发文量
998
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