Nondestructive Leaf Area Estimation for Chia

Abstract

Core Ideas Leaf area in chia cannot be accurately predicted by the product of leaf width and length. Regressing leaf area log linearly on width and length accounts for change of shape with size. We provide accurate prediction models valid across experiments, populations, and N levels. Mixed‐model meta‐regression allows integrating leaf area data across experiments. Leaf area (LA) is an important agronomic trait but is difficult to measure directly. It is therefore of interest to estimate LA indirectly using easily measured correlated traits. The most commonly used approach to predict LA uses the product of leaf width (LW) and leaf length (LL) as single predictor variable. However, this approach is insufficient to estimate LA of chia (Salvia hispanica L.) because the leaves are differently shaped depending on leaf size. The objectives of this study were to develop a nondestructive LA estimation model for chia using LW and LL measurements that can take differences in leaf shape into account and to determine whether population and nitrogen fertilizer level have an effect on the accuracy of a LA estimation model. A total of 840 leaves were collected from five different field experiments in 2015 and 2016 conducted in southwestern Germany. The experiments comprised eight populations of black‐ and white‐seeded chia (07015 ARG, 06815 BOL, 06915 ARG, G8, G7, G3, W13.1, and Sahi Alba 914) and three nitrogen fertilizer levels (0, 20, and 40 kg N ha−1). We used meta‐regression to integrate the data accounting for heterogeneity between experiments, populations, and nitrogen levels. The proposed LA estimation model with two measured predictor variables (LL and LW) was LA = 0.740 × LL0.866 × LW1.075 and provided the highest accuracy across populations and nitrogen levels [r = 0.989; mean squared deviation (MSD) = 2.944 cm4]. The model LA = 1.396 × LW1.806 with only LW was almost as accurate (r = 0.977; MSD = 5.831 cm4).

Allowing for the structure of a designed experiment when estimating and testing trait correlations

“…Example 4 provides a case where the marginal correlation is the primary focus because good predictions are sought for leaf area at the level of individual observations. If the marginal correlation is high, which was the case here, one may fit a regression model for prediction (Mack et al 2017). Conversely, when correlations are deemed to be substantially heterogeneous between effects, it is necessary to study correlations at the level of individual effects.…”

Digital image processing method for estimating leaf length and width tested using kiwifruit leaves (Actinidia chinensis Planch)

“…The length of the kiwifruit leaf (pixels) was acquired using the two-point distance formula given in Eq (1). The width of the kiwifruit leaf (pixels) was measured in terms of the number of pixels between the two edge points where the lamina is the widest.…”

“…The surface area of a plant leaf is a crucial parameter because it influences the interception of radiation, water and energy exchange processes, and crop growth and productivity [1]. Several researchers have therefore aimed to precisely measure leaf areas, which have traditionally been estimated quantitatively through both direct and indirect methods [2].…”

Pendugaan Luas Daun Tanaman Talas (Colocasia esculenta)

“…Pendugaan luas daun tanaman Crotalaria juncea dengan menggunakan model Ŷ=0,7390 P x L dengan koefisien determinasi R 2 = 0,9849 (Juliana et al 2017). Mack et al (2017) Talas Kalimantan 0,91 a 57,63 99,92 0,890,93 Keterangan: Nilai koefisien regresi yang diikuti oleh huruf yang sama menunjukkan tidak ada perbedaan yang nyata menggunakan dua peubah panjang dan lebar daun juga lebih baik pada tanaman chia (Salvia hispanica L.). Demikian pula pendugaan luas daun untuk tanaman Juglans regia L. (Keramatloua et al 2015), Vernonia ferruginea Less (Souza & Amaral 2015) dan tanaman sayuran (Mikias 2016) dengan menggunakan dua peubah, yaitu panjang dan lebar daun.…”