A scale-invariant lettuce leaf area calculation using machine vision and knowledge-based methods
Keywords:Knowledge-Based, Lettuce Leaf Area, Machine Vision, Scale Invariance.
Leaf area is one of the most significant reference tools to characterize plant growth and predict growth stages. Scale invariance in calculating leaf area needs to be understood in the lettuce growth monitoring context. Using machine vision and knowledge-based classifiers, this research produced a system for a scale invariant area calculation of lettuce leaf area by detecting a template marker with a known area for scaling area measurements. Results showed that knowledge-based algorithm can improve the performance of the machine vision classifiers for rejecting false positives even for a limited number of training datasets. Area measurements produced by the system performed well in terms of root-mean-square error (RMSE).
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