Pea shoots are young, tender vine tips of garden peas (Pisum sativum L.), and they have been recognized as a popular specialty vegetable in some parts of Asia and Africa (Lim, 2012). They are eaten fresh, stir fried, lightly steamed, or sautéed (Miles et al., 2018). Nowadays, consumption of pea shoots as a part of a healthy diet has become increasingly popular worldwide (Miles et al., 2018; Santos et al., 2014), because they are believed to be rich in health-promoting phytochemicals, especially antioxidants such as vitamin C, carotenoids, and various phenolic compounds (Liu et al., 2014; Santos et al., 2014).
Pea shoots can be harvested many times throughout the growing season (Miles et al., 2018). However, pea shoot production has been also limited by a few factors. One of the limiting factors is that the harvesting is labor intensive. Currently, pea shoots are mainly harvested manually, which contributes greatly to production costs in labor-expensive regions (Miles et al., 2018). Another factor is the fluctuation of yield among different harvests, because the growth of pea shoots is not uniform. This growth factor increases the difficulty in predicting crop yield before each harvest.
Nondestructive estimation of individual shoot fresh weight (FW), using its measurable morphological traits, is useful for a wide variety of purposes in pea shoot production. For example, it could potentially be used for robotic harvesting in identifying and selecting the proper target shoots with the aid of a vision system (Ruiz-Altisent et al., 2004), because quick estimation of plant sizes from their two-dimensional images is becoming possible (Golzarian et al., 2011). In addition, it is important to predict the yield of pea shoots before each harvest, so the growers can determine if they are likely to fulfill market orders in advance of harvest. Developing a nondestructive approach to estimate individual shoot weight is the first step to predict crop yield, because a destructive approach is not appropriate for long-lived plants with complex structures (Spencer et al., 2006).
Both nonlinear and linear models have been used for estimation of plant biomass from measurable morphological traits. For nonlinear models, power functions, with the general form of y = axb, are very useful in relating plant weight to its height or diameter (Fehrmann and Kleinn, 2006; Pilli et al., 2006; Spencer et al., 2006). In the above functions, x is normally breast-height diameter for estimating aboveground biomass of trees (Pilli et al., 2006), and plant height for estimating aboveground biomass of aquatic plants (Spencer et al., 2006; Thursby et al., 2002). However, the power functions in the above studies are used for estimating dry weight of aboveground biomass. This raised a question about the adaptation of the nonlinear functions to estimate FW of shoot tips (e.g., pea shoots). Nevertheless, in the estimation of fresh biomass of tea’s shoot tip, a linear relationship has been obtained between shoot weight and some morphological variables such as shoot length, leaf number, and bud length (Sun et al., 2007). However, tea is a woody plant, and its shoot tip has different morphological traits from pea shoots, so even when using a linear regression model, the included variables may be different between the two species.
Although quite a few studies have been performed to estimate plant biomass (especially dry biomass) from measurable morphological traits by using both nonlinear and linear models, the related information has so far been unavailable for pea shoots. The objective of this study was to develop effective regression models for estimating FW of individual pea shoots based on its measurable morphological traits, by screening and selecting suitable model forms and variables to include.
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