Non-destructive estimation of leaf area in hairy fleabane (Conyza bonariensis)

Hairy fleabane (Conyza bonariensis) is a widespread and troublesome weed in agricultural systems, and estimating its leaf area (LA) is essential for growth analysis and weed management studies. This study aimed to develop equations to estimate the LA based on the linear dimensions of leaf blades. The relationships between the LA and the dimensional parameters of leaf blade (length and width) were studied in C. bonariensis var. angustifolia and C. bonariensis var. bonariensis. Both linear and power regression models were tested, and their performance was assessed using Root Mean Square Error (RMSE), index of agreement (dw), BIAS index, Pearson’s linear coefficient (r), and index of confidence or performance (c). The best-performing model used the product of length and width (L×W), yielding the highest r values and the lowest RMSE for both botanical varieties. Specific models provided better estimates than the general model. The linear equations LA = 0.6578 × (L×W) and LA = 0.5896 × (L×W) for C. bonariensis var. angustifolia and C. bonariensis var. bonariensis, respectively, were the most accurate for estimating their LA. These equations offer reliable, non-destructive tools for estimating LA in studies involving each variety, contributing to improved precision in weed science research.

Authors

Institutions

Publication Details

Journal
Revista Facultad Nacional de Agronomía Medellín
Published
2025-09-01
DOI
https://doi.org/10.15446/rfnam.v78n3.118970
Primary Topic
Leaf Properties and Growth Measurement
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Non-destructive estimation of leaf area in hairy fleabane (Conyza bonariensis)

Ary José Duarte, Lilian Osmari Uhlmann, André da Rosa Ulguim, Glauco Pacheco Leães et al.
Revista Facultad Nacional de Agronomía Medellín
Leaf Properties and Growth Measurement
article

Non-destructive estimation of leaf area in hairy fleabane (Conyza bonariensis)

Ary José Duarte, Lilian Osmari Uhlmann, André da Rosa Ulguim, Glauco Pacheco Leães, Mariana Macedo, Tilio Adan Lucas, Bruno Mussoi Cavichiloi
article en

Abstract

Hairy fleabane (Conyza bonariensis) is a widespread and troublesome weed in agricultural systems, and estimating its leaf area (LA) is essential for growth analysis and weed management studies. This study aimed to develop equations to estimate the LA based on the linear dimensions of leaf blades. The relationships between the LA and the dimensional parameters of leaf blade (length and width) were studied in C. bonariensis var. angustifolia and C. bonariensis var. bonariensis. Both linear and power regression models were tested, and their performance was assessed using Root Mean Square Error (RMSE), index of agreement (dw), BIAS index, Pearson’s linear coefficient (r), and index of confidence or performance (c). The best-performing model used the product of length and width (L×W), yielding the highest r values and the lowest RMSE for both botanical varieties. Specific models provided better estimates than the general model. The linear equations LA = 0.6578 × (L×W) and LA = 0.5896 × (L×W) for C. bonariensis var. angustifolia and C. bonariensis var. bonariensis, respectively, were the most accurate for estimating their LA. These equations offer reliable, non-destructive tools for estimating LA in studies involving each variety, contributing to improved precision in weed science research.

Revista Facultad Nacional de Agronomía MedellínVol. 78(3)
Universidade Federal de Santa Maria (BR)
Universidade Federal de Santa Maria
Openalex Percentile: Top 83%
Leaf Properties and Growth Measurement
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.