Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision

Abstract

The harvesting time of fresh tea leaves has a significant impact on product yield and quality. The aim of this study was to propose a method for real-time monitoring of the optimum harvesting time for picking fresh tea leaves based on machine vision. Firstly, the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm, filtering with a median filter algorithm, binary processing with the Otsu algorithm, and noise reduction and edge smoothing using open and close operations. Then the leaf characteristics, such as leaf area index, average length, and leaf identification index, were calculated. Based on these, the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status. When this method was applied to a RGB tea-tree canopy image acquired at 45° shooting angle, the fresh tea-leaf recognition rate was 90.3%, and the accuracy for fresh tea-leaf harvesting status was 98% by cross validation. Hence, this method provides the basic conditions for future tea-plantation operation and management using information technology, automation, and intelligent systems. Keywords: agricultural machinery, fresh tea leaves, machine vision, intelligent recognition, real-time monitoring DOI: 10.25165/j.ijabe.20191201.3418 Citation: Zhang L, Zhang H D, Chen Y D, Dai S H, Li X M, Imou K, Liu Z H, et al. Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision. Int J Agric & Biol Eng, 2019; 12(1): 6–9.

Plant phenotyping relevance

茶樹キャノピー画像から葉形状・葉面積指数・平均長などの植物形質を抽出し、収穫適期を判定する機械視覚手法を開発・検証しており、フェノタイピング手法が中心である。

Code and data availability

The supplied article blocks describe tea-canopy image acquisition and Bayesian discriminant analysis but contain no data availability statement, no public dataset or image repository, and no author code or model deposit. Analysis was done in LabVIEW/SPSS with no shared artifacts mentioned.

No evidence-backed public reproduction asset is currently recorded.