Machine Vision Systems for Plant Growth Measurement and Modeling

Abstract

Recent developments of microcomputer-based machine vision systems has offered convenient and non-destructive ways for measurements of plant characteristics that allow plant growth assessment. This paper presents brief reviews and comparisons of machine vision approaches for non-destructive plant growth measurement. The simple approach of using projected silhouette image of a plant was most commonly used in acquiring growth data in various experiments. Plant fresh or dry weight can be indirectly estimated from projected leaf area by calibration with data from standard measurement methods. However, the estimation error usually increases as the extent of overlapped leaves increases. Images acquired from different views or utilizing dual cameras allow the estimation of leaf area without predetermined calibration relationship but the image processing algorithms usually become more sophisticated. By incorporating multiple images of a plant, three-dimensional and structural information may be extracted for more detailed growth analyses and modeling.

Development and Performance Test of a Height-Adaptive Pesticide Spraying System

“…The results show that both ultrasonic and LIDAR have a good correlation with manual method in aspect of volume estimation. Furthermore, in [20], Lin et al use silhouetted plant image to extract plant information, for example, plant height and leaf area. All the experiments are carried out without destroying the plant itself.…”