Abstract

Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process.

Highlights

  • In capital-intensive horticultural cropping systems, estimating production or the production potential is essential in scheduling management decisions

  • In 2013, a rain surplus was present until 100 days before harvest (DOY 150) or the beginning of Stage II of fruit development, which is mostly associated with vegetative growth [3]

  • The results indicated a good correlation between spectral measurements and production variables, this relationship was dependent on the growing season (Figures 4 and 5)

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Summary

Introduction

In capital-intensive horticultural cropping systems, estimating production or the production potential is essential in scheduling management decisions (i.e., fruit thinning, harvest, etc.). One of the difficulties is the variable influence of contributing factors on fruit yield and quality during different phenological stages (review by [1]). Traditional in situ measurements of production variables and biophysical variables are time consuming and labor intensive. This results in limited samples and repetitions, which are insufficient to account for the high spatial and temporal variability within and between orchards [4,5]. It is yet well acknowledged that remote sensing can provide non-destructive, time efficient and cost beneficial alternatives for horticulture [6,7,8]

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