The Application Status and Trends of Machine Vision in Tea Production

Abstract

The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.

Design of a Mulitmodal Detection System and Its First Application in Tea Selection Process

“…The information obtained by a single sensor is limited and often cannot meet the needs of classification. Machine vision system with multiple types of sensors participating in information integration will fill this gap [22].…”

Design of a Multimodal Detection System Tested on Tea Impurity Detection

“…The information obtained by a single sensor is limited and often cannot meet the needs of classification. Machine vision system with multiple types of sensors participating in the information integration will fill this gap [37]. A multispectral camera can obtain the spectral information of tea, but its resolution is too low to meet the needs of detecting small impurities.…”