计算机科学
过程(计算)
天蓬
吞吐量
人工智能
农业工程
植物冠层
计算机视觉
领域(数学)
作物
精准农业
环境科学
遥感
农业
数学
农学
工程类
生物
地理
生态学
电信
操作系统
纯数学
无线
作者
Annalisa Milella,Roberto Marani,Antonio Petitti,Giulio Reina
标识
DOI:10.1016/j.compag.2018.11.026
摘要
Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted onboard an agricultural vehicle.
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