Optimization of Leaf Area Estimation in a High-Density Apple Orchard Using Hemispherical Photography

in HortScience

Leaf area is evaluated as leaf area index (LAI), the ratio of leaf to ground area, and is known to be crucial to understanding forests and high-quality fruit production in orchards. Nondestructive tools have been available for decades that pair hemispherical photography with gap fraction theories to understand LAI. Those tools do not allow for rapid assessment in the field, and there is no standardized protocol to acquire accurate estimates yet. This experiment has developed an optimized method with the CID Plant Canopy Imager (CI-110) in a high-density apple orchard. This novel tool for LAI estimation allows image acquisition and processing in real time in the field. LAI assessments of hemispherical images were taken under five light environments, at three imaging heights, processed with two thresholding methods, and were compared with destructive LAI values for accuracy. The difference between estimated and destructive LAI (∆LAI) was determined for trees on an individual or grouped by a three tree basis. Estimations for triplet groupings were more accurate, and the significantly lower ∆LAI in each treatment occurred for the no-net environment, 10 cm from the ground and processed with the Otsu threshold. When combined as triplet groupings, this methodology sequence yielded an LAI estimation with a 13% prediction error (∆LAI = 0.19). The use of the CI-110 with this methodology can give useful, real-time information regarding orchard canopies to address pruning and training decisions for high-quality fruit production.

Contributor Notes

This research was funded by the Washington Tree Fruit Research Commission with project number # AP14-103A and WSDA Specialty Crop Block Grant Agreement Number: K1767.

Mention of a proprietary product or vendor does not constitute a guarantee or warranty of the product by Washington State University and does not imply its approval to the exclusion of other products or vendors that also may be suitable.

These authors contributed equally to this work and are co-authors.

Corresponding author. E-mail: Stefano.musacchi@wsu.edu.

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    The Digital Plant Canopy Imager CI-110 set up in the field features a self-leveling camera attached to a bar with 24 photosynthetically active radiation sensors and displays measurements in real time on an attached tablet through CI-110 Plant Canopy Analysis System software.

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    A comparison of the difference between estimated leaf area index (LAI) and defoliated LAI for 30 combinations of light environment, imaging height, and leaf distinguishing thresholds for (A) individual trees (estimated LAIindividual − destructive LAIindividual) and (B) and triplets (|Average ∑LAIestimated| − |Average ∑LAIdestructive|). Significance established with an analysis of variance proc GLM procedure and type III sum of squares in SAS. Mean separation by Student–Newman–Keuls’ post hoc pairwise tests at P < 0.05. Bars are Standard Error (SE). Columns identifying the best combinations are in gray.

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    The effect of light intensity, shade netting, and height on leaf area index (LAI) through pairs of hemispherical images (AB, CD, and EF) before and after processing with the CI-110 Plant Canopy Analysis System for ‘WA38’ apple trees. Pairs (A and B) and (C and D) demonstrate the effect of light intensity during image acquisition. Images (A and B) showcase the leaf washout effect because of direct solar interference and the high reflectance of the pearl 50% shade net. Under lower light intensity, foliage detection by the software is enhanced (C and D). Pairs (C and D) and (E and F) demonstrate the effect imaging height has on LAI, it is artificially inflated from 10 cm (D) to 40 cm (F).

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