Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

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 OPEN ACCESSRemote Sens. 2015, 7 9887 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.

Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

“…For the irrigated orchard, a significant decrease in R 2 values was the result of both sunlit and shaded canopies within one scene, as both 41 and 131˝row orientations were monitored. The increased influence of shadow compared to the synthetic imagery was caused by a decrease in illumination elevation and the presence of a hedgerow cropping system causing one predominantly shaded and sunlit side [41,75]. Several studies have shown the negative effect of shaded canopy parts on the correlation between vegetation indices and biophysical variables [3,4,76].…”

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

“…Previous studies have demonstrated the importance of spectral indices related to biochemical, structural, physiological parameters and water stress as direct and indirect indicators of fruit yield and quality [18,[47][48][49]. Therefore, using the preprocessed canopy reflectance spectra, a total of 20 vegetation indices were calculated, and all the indices were used to calibrate prediction models ( Table 1).…”

“…Serrano et al [16] suggested the suitability of the water index (WI) to predict the berry quality of grapevines grown in rainfed conditions. In addition, photochemical reflectance index (PRI) [8], an indicator of epoxidation state of the xanthophyll cycle pigments and non-photochemical quenching (NPQ), was found to be related to the fruit quality parameter in citrus and pear orchards [17,18]. There is no consensus on the effectiveness of a single index.…”

Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia

“…Whilst these technologies have been found to be highly effective for measuring yield in row crops [16][17][18][19][20], generally attributed to harvest index (HI) (i.e., fraction of biomass allocated to yield components divided by the total above ground biomass) [21][22][23], similar studies in perennial fruit tree crops, such as citrus [24,25], apple [7,26], pear [27], peach [28], olives [28], mango [29], and grapevines [30,31] have produced varying levels of success. For avocado, there has only been limited remote sensing research investigating fruit size and yield mapping as well as tree number auditing [4].…”