As the Pacific Northwest fruit industry shifts to precision-based management strategies for their orchards, tools are needed to understand tree physiology in the orchard to optimize fruit quality. Leaf area index is the ratio between the summed area of one side of each leaf and the ground area the tree occupies (Bréda, 2003). LAI is an important physiological parameter within an orchard because it is easily manipulated with training and pruning, and can significantly affect canopy microclimates, water balance, gas exchange, and photosynthetic efficiency (Bréda, 2003; Robinson et al., 1991; Zhang et al., 2005). Correlations exist between LAI and canopy development (Liu et al., 2013), biomass (Goswami et al., 2015), ecosystem productivity (Bréda, 2003), light interception (Barritt, 1989; Liu et al., 2013), fruit quality (Robinson et al., 1983), and yield (Barritt, 1989; Robinson et al., 1993). A canopy’s LAI is associated with its efficiency and physiology (Faust 1989; Robinson et al., 1991). Jackson (1978) suggests that optimal LAI values for production are between 1.2 and 2.0. Verheij and Verwer (1973) suggested an optimal LAI value of 2.1 for ‘Golden Delicious’. However, if LAI values are too high, excessive shading can occur in the canopy, reducing flower bud initiation and fruit coloration (Heinicke, 1964).
The importance of LAI has led to the pursuit of improved quantification methodologies.
There are two strategies for LAI quantification: direct and indirect. Direct quantification is based on the destructive defoliation of trees, or the process of collecting the leaves as they naturally abscise (Bréda, 2003). Although the direct method is accurate, it is also highly time consuming, labor intensive, not always economically viable, and LAI cannot be calculated in the field (Chianucci and Cutini, 2012). Indirect quantification of LAI can be accomplished through the rapid estimation of a canopy’s leaf area through photographs (Lakso, 1980) which are processed with gap fraction theory to determine canopy porosity and shape (Zhang et al., 2005). Commonly in forestry, digital cameras are equipped with fisheye, or hemispherical, lenses and paired with gap fraction models to estimate LAI and evaluate forest ecosystems and model ecophysiology (Bréda, 2003; Chianucci and Cutini, 2012).
LAI estimation techniques for forestry are used in other environments like orchards (Liu et al., 2013; Poblete-Echeverría et al., 2015; Wünsche et al., 1995), but they must be adjusted and optimized because the trees are in rows and can be much smaller than in forests. The modern choice for new orchards is high-density planting (HDP) at 2000–4000 trees/ha with planting densities as high as 0.75 m × 3.00 m. HDP is characterized by small and narrow canopies where a large part of the foliage is fully exposed to the sun as typically arranged to a spindle or V system (Musacchi and Green, 2017). However, the use of estimation tools in both environments has opportunities for error throughout the application.
Sources of error in LAI estimation occur during image acquisition, or when analyzing photographs (Chianucci and Cutini, 2012; Rich, 1988). Guidelines, methods, and software for forestry applications were reviewed and suggested in Chianucci and Cutini (2012), Jonckheere et al., (2004), and Bréda (2003). Ideal light conditions for measurements are uniform like overcast, early morning, or evening skies (Lakso, 1976). Cutini and Varallo (2006) acquired images of two forest species at heights of 1 m from the ground. After images are acquired, software like WinSCANOPY are available to enable automatic pixel classification to estimate LAI. However, there is no standardized methodology, tool, or model for ground-based LAI estimation for forests or orchards (Jonckheere et al., 2004; Woodgate et al., 2012).
Therefore, a streamlined methodology and sampling strategy is needed for each instrument and environment to limit bias and create reproducible and consistent results. The Digital Plant Canopy Imager CI-110 (CID Bio-Science, Inc., Camas, WA) was developed for rapid estimations of LAI by combining light sensors, hemispherical photography, and processing software in one tool. The CI-110 LAI estimation tool was selected in this study for its simplicity and data accessibility through an associated tablet with on-screen viewing for accurate image acquisition and portability (Fig. 1). The CI-110 differs when compared with other hemispherical photography analysis systems because it can acquire and process images instantly in the field without additional external software, like WinSCANOPY (Regents Instruments, Inc., Quebec, Canada) and HemiView (Delta-T Devices, Ltd., Cambridge, UK) (Bréda, 2003). Instead, images acquired by CI-110 are processed by the built-in models Otsu or Entropy Crossover provided in CID Plant Canopy Analysis software (CID Bio-Science, Inc.). In Bréda’s (2003) review on ground-based LAI instruments, it was noted that there were no published data comparing the CI-110 and its integrated software to other tools. Since then, studies have demonstrated the accuracy of the CI-110 in forestry estimations (Keane et al., 2005), but no literature was found regarding the use of this tool in tree fruit orchards. Orchards are very different from forests given their density, arrangement, and tree architecture. The objective of this experiment is to determine an optimal methodology for light conditions, height from ground, and leaf distinguishing thresholds for the CI-110 to estimate LAI accurately within an apple HDP orchard.
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