All Issue

2018 Vol.36, Issue 2 Preview Page
Estimation of Leaf Area in Paprika Based on Leaf Length, Leaf Width, and Node Number Using Regression Models and an Artificial Neural Network
Joonwoo Lee1
,
Taewon Moon1
,
Du Sung Nam1
,
Kyoung Sub Park2
,
Jung Eek Son1*
1Department of Plant Science and Research Institute of Agriculture and Life Sciences, Seoul National University
2Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science
*교신저자. *Corresponding Author. 
30 April 2018. pp. 183-192
PDF XML Citation
Abstract

Leaf area directly affects growth responses and plays an important role in estimating individual leaf growth. Most studies on the subject have used non-destructive estimations of leaf area based on regression analysis of leaf length and width, with the assumption that the leaf shape is constant. For paprika, however, leaf shapes differ depending on the nodes where leaves are attached. The objective of this study was to estimate leaf area using not only the leaf length and width but also the node number. Paprika leaves were collected ten months after transplanting, and the leaf length, width, area, and shape ratio (= leaf length/width), as well as node number, were measured. Leaf length and width measurements led to the development of regression equations; among them, equations with strong correlations were chosen and used in validation. The measured leaf length and width and node number were used to train a selected artificial neural network (ANN, Google Tensorflow). A regression equation using only leaf area and width estimated leaf areas with high accuracy, while the accuracy significantly decreased when the equation was separately applied to the upper and lower leaves. This result was likely due to the shape characteristics of the leaves; newly-formed leaves were thin and long, whereas those of developed leaves were broad and thick. Therefore, the length/width ratios of the upper and lower leaves were different. The regressions including the node number in the model resulted in higher R2 values with higher estimation accuracy than the previous regression equations for a variety of leaf positions. The ANN estimated areas of leaves located in a variety of positions with higher accuracy using a simpler process than both regression equations. In conclusion, not only the leaf length and width but also the node number are important to estimate leaf area in paprika, and ANN is an effective tool to analyze growth characteristics using various indicators.

Keywords
canopy structure
leaf length-width ratio
leaf shape
machine learning
non-destructive method

References
1
Aminifard MH, Aroiee H, Ameri A, Fatemi, H (2012) Effect of plant density and nitrogen fertilizer on growth, yield and fruit quality of sweet pepper (Capsicum annuum L.). Afr J Agric Res 7:859-866
2
Antunes WC, Pompelli MF, Carretero DM, DaMatta FM (2008) Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora). Ann Appl Biol 153:33-40. doi.org/10.1111/j.1744-7348.2008.00235.x
3
Blanco FF, Folegatti MV (2005) Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Sci Agric 62:305-309. doi.org/10.1590/S0103-90162005000400001
4
Cho YY, Oh SB, Oh MM, Son JE (2007) Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. Sci Hortic 111:330-334. doi.org/10.1016/j.scienta.2006. 12.028
5
de Swart EAM, Groenwold R, Kanne HJ, Stam P, Marcelis LFM, Voorrips RE (2004) Non-destructive estimation of leaf area for different plant ages and accessions of Capsicum annuum L. J Hortic Sci Biotechnol 79:764-770. doi.org/10.1080/14620316.2004.11511840
6
Díaz-Pérez JC (2013) Bell pepper (Capsicum annuum L.) crop as affected by shade level: Microenvironment, plant growth, leaf gas exchange, and leaf mineral nutrient concentration. HortScience 48:175-182
7
Díaz-Pérez JC (2014) Bell Pepper (Capsicum annuum L.) crop as affected by shade level: Fruit yield, quality, and postharvest attributes, and incidence of Phytophthora blight (caused by Phytophthora capsici Leon.). HortScience 49:891-900
8
Dickinson TA, Parker WH, Strauss RE (1987) Another approach to leaf shape comparisons. Taxon 36:1-20. doi.org/10.2307/1221345
9
Ferrara A, Lovelli S, Di Tommaso T., Perniola M (2011) Flowering, growth and fruit setting in greenhouse bell pepper under water stress. J Agron 10:12-19. doi.org/10.3923/ja.2011.12.19
10
Gamiely S, Randle WM, Mills HA, Smittle DA (1991) Greenhouse energy consumption prediction using neural networks models. Int J Agric Biol 11:1-6
11
González-Real MM, Liu HQ, Baille A (2009) Influence of fruit sink strength on the distribution of leaf photosynthetic traits in fruit-bearing shoots of pepper plants (Capsicum annuum L.). Environ Exp Bot 66:195-202. doi.org/10.1016/j.envexpbot.2009.01.005
12
Jung DH, Cho YY, Lee JG, Son JE (2016) Estimation of leaf area, leaf fresh weight, and leaf dry weight of irwin mango grown in greenhouse using leaf length, leaf width, petiole length, and SPAD value. Protected Hortic Plant Fac 25:146-152. doi.org/10.12791/ KSBEC.2016.25.3.146
13
Jung DH, Kim D, Yoon HI, Moon TW, Park KS, Son JE (2016) Modeling the canopy photosynthetic rate of romaine lettuce (Lactuca sativa L.) grown in a plant factory at varying CO2 concentrations and growth stages. Hortic Environ Biotechnol 57:487-492
14
Launay M, Guérif M (2003) Ability for a model to predict crop production variability at the regional scale: an evaluation for sugar beet. Agronomie 23:135-146. doi.org/10.1051/agro:2002078
15
Lohr VI, Sudkamp AB (1989) Pruning responses of tissue-cultured plantlets of Rhododendrons. J Environ Hortic 7:23-25
16
Lu HY, Lu CT, Wei ML, Chan LF (2004) Comparison of different models for nondestructive leaf area estimation in taro. Agron J 96:448-453. doi.org/10.2134/agronj2004.4480
17
Marcelis LFM, Heuvelink E, Baan Hofman-Eijer LR, Den Bakker J, Xue LB (2004) Flower and fruit abortion in sweet pepper in relation to source and sink strength. J Exp Bot 55:2261-2268. doi.org/10.1093/jxb/erh245
18
Montero F, de Juan JA, Cuesta A, Brasa A (2000) Nondestructive methods to estimate leaf area in Vitis vinifera L. HortScience 35:696-698
19
Park KS, Bekhzod K, Kwon JK, Son JE (2016) Development of a coupled photosynthetic model of sweet basil hydroponically grown in plant factories. Hortic Environ Biotechnol 57:20-26
20
Patanè C (2011) Leaf area index, leaf transpiration and stomatal conductance as affected by soil water deficit and VPD in processing tomato in semi-arid Mediterranean climate. J Agron Crop Sci 197:165-176. doi.org/10.1111/j.1439-037X.2010.00454.x
21
Peksen E (2007) Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Sci Hortic 113:322–328. doi.org/10.1016/ j.scienta.2007.04.003
22
Rosenthal WD, Vanderlip RL (2004) Simulation of individual leaf areas in grain sorghum. Agronomie 24:493-501. doi.org/10.1051/ agro:2004046
23
Serdar U, Demirsoy H (2006) Non-destructive leaf area estimation in chestnut. Sci Hortic 108:227-230. doi.org/10.1016/j.scienta. 2006.01.025
24
Sharma VK, Semwal CS, Uniyal SP (2010) Genetic variability and character association analysis in bell pepper (Capsicum annuum L.). J Hortic Forest 2:58-65
25
Shin JH, Ahn TI, Son JE (2011) Modeling of transpiration of paprika (Capsicum annuum L.) plants based on radiation and leaf area index in soilless culture. Hortic Environ Biotechnol 52:265-269. doi.org/10.1007/s13580-011-0216-3
26
Tai NH, Ahn TI, Park JS, Son JE (2009) Estimation of leaf area, fresh weight, and dry weight of Paprika (Capsicum annuum L.) using leaf length and width in rockwool-based soilless culture. Hortic Environ Biotechnol 50:422-426
27
Taormina R, Chau KW (2015) Neural network river forecasting with multi-objective fully informed particle swarm optimization. J Hydroinform 17:99-113. doi.org/10.2166/hydro.2014.116
28
Vaidyanathan S (2015) 3-cells cellular neural network (CNN) attractor and its adaptive biological control. Int J Pharmtech Res 8:632-640
29
Wang T, Gao H, Qiu J (2016) A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Netw Learn Syst 27:416-425. doi.org/10.1109/TNNLS.2015.2411671
30
Weight C, Parnham D, Waites R (2008) Technical advance: LeafAnalyser: a computational method for rapid and large‐scale analyses of leaf shape variation. Plant J 53: 578-586. doi.org/10.1111/j.1365-313X.2007.03330.x
31
Xia C, Lee JM, Li Y, Song YH, Chung BK, Chon TS (2013) Plant leaf detection using modified active shape models. Biosyst Eng 116:23-35. doi.org/10.1016/j.biosystemseng.2013.06.003
Information
  • Publisher :KOREAN SOCIETY FOR HORTICULTURAL SCIENCE
  • Publisher(Ko) :한국원예학회
  • Journal Title :Horticultural Science and Technology
  • Journal Title(Ko) :원예과학기술지
  • Volume : 36
  • No :2
  • Pages :183-192
  • Received Date : 2017-10-10
  • Revised Date : 2017-11-06
  • Accepted Date : 2017-11-04
  • DOI :https://doi.org/10.12972/kjhst.20180019
Journal Informaiton Horticultural Science and Technology Horticultural Science and Technology
Online Submission
  • scopus
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close