Area estimation of soybean leaves of different shapes with artificial neural networks

Abstract

Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.

Screening the Soybean Varietal Lines for Selecting High-Yielding and Better Agronomic Traits Producing lines

“…The leaf area has been estimated using the leaf blade's length and width (de Sá et al, 2022). The lack of a significant difference in leaf area between summer and spring suggests that environmental factors specific to these seasons may not have a substantial impact on the overall leaf area of soybean plants in this study.…”