Comparison between computer recognition and manual measurement methods for the estimation of leaf area

Author:

Mu Youying1,He Ke2,Shi Peijian1,Wang Lin3,Deng Linli3,Shi Zhuyue1,Liu Mengdi1,Niklas Karl J4

Affiliation:

1. College of Biology and the Environment, Nanjing Forestry University , 159 Longpan Road, Nanjing 210037 , China

2. Architectural Design and Research Institute, Shenzhen University , 3688 Nanhai Avenue, Shenzhen 518000 , China

3. Department of Applied Mathematics, College of Science, Nanjing Forestry University , 159 Longpan Road, Nanjing 210037 , China

4. School of Integrative Plant Science, Cornell University , Ithaca, NY 14853 , USA

Abstract

Abstract Background and Aims Leaf area (A) is a crucial indicator of the photosynthetic capacity of plants. The Montgomery equation (ME), which hypothesizes that A is proportional to the product of leaf length (L) and width (W), is a valid tool for non-destructively measuring A for many broadleaved plants. At present, the methods used to compute L and W for the ME can be broadly divided into two kinds: using computer recognition and measuring manually. However, the potential difference in the prediction accuracy using either method has not been thoroughly examined in previous studies. Methods In the present study, we measured 540 Alangium chinense leaves, 489 Liquidambar formosana leaves and 215 Liriodendron × sinoamericanum leaves, utilizing computer recognition and manual measurement methods to determine L and W. The ME was used to fit the data determined by the two methods, and the goodness of fits were compared. The prediction errors of A were analysed by examining the correlations with two leaf symmetry indices (areal ratio of the left side to the right side, and standardized index for bilateral asymmetry), as well as the leaf shape complexity index (the leaf dissection index). Key Results The results indicate that there is a neglectable difference in the estimation of A between the two methods. This further validates that the ME is an effective method for estimating A in broadleaved tree species, including those with lobes. Additionally, leaf shape complexity significantly influenced the estimation of A. Conclusions These results show that the use of computer recognition and manual measurement in the field are both effective and feasible, although the influence of leaf shape complexity should be considered when applying the ME to estimate A in the future.

Publisher

Oxford University Press (OUP)

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