Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks

被引:11
|
作者
Arunachalam, Ajay [1 ]
Andreasson, Henrik [1 ]
机构
[1] Orebro Univ, Ctr Appl Autonomous Sensor Syst AASS, Orebro, Sweden
关键词
Phenotype; Image processing; Spectral; Robotics; Object localization; Precision agriculture; Plant science; Pattern recognition; Computer vision; Automation; Perception; LEAF PIGMENT CONTENT; SYSTEM; GROWTH;
D O I
10.1016/j.compeleceng.2021.107098
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Plant phenotyping in general refers to quantitative estimation of the plant's anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
引用
收藏
页数:12
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