Estimation of Leaf Area in Paprika Based on Leaf Length, Leaf Width, and Node Number Using Regression Models and an Artificial Neural Network

• Published in: Weon'ye gwahag gi'sulji Vol. 36; no. 2; pp. 183 - 192
• Main Authors: Lee, Joonwoo, Moon, Taewon, Nam, Du Sung, Park, Kyoung Sub, Son, Jung Eek
• Format: Journal Article
• Language: English
• Published: 한국원예학회HST 01.04.2018; 한국원예학회
• Subjects: 농학; leaf length-width ratio; machine learning; leaf shape; non-destructive method; canopy structure
• ISSN: 1226-8763; 2465-8588
• DOI: 10.12972/kjhst.20180019

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. Leaflength 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. KCI Citation Count: 9