Improved Yield and Fruit Quality Estimation in Pear Orchards Using Remote Sensing Time Series

Yield and fruit quality estimation provides vital information to orchard managers, but requires time consuming, labor intensive and destructive in situ measurements. Large amounts of these point measurements are required, as both fruit quality (i.e., fruit firmness and sugar content) and yield (i.e., total production) are related to growing conditions (i.e., meteorological, soil moisture, crop load, etc.) during the growing season. An alternative to these point measurements is the use of remote sensing. Traditional yield monitoring through remote sensing uses measurements at harvest (orchards) or at full cover growing stage (crops). This study investigated the potential of remote sensing for crop load predictions throughout the growing season. Therefore, an irrigated and rainfed orchard, trained in a V-system and a spindle bush system, respectively, were monitored for three consecutive growing seasons. This remote sensing time series was linked to qualitative (i.e., fruit firmness and total soluble solids (TSS)) and quantitative fruit properties (i.e., total production and number of fruits). Overall, the remote sensing measurements were found significantly correlated with both fruit quality and yield, although the correlation was variable throughout the growing season and this temporal dependency varied between orchards. In the rainfed orchard, index values during vegetative growth periods (100 days after bloom) presented high positive correlation with yield (r>0.8), which decreased towards harvest. In the irrigated orchard, index values near harvest showed an increasingly positive correlation with fruit firmness and TSS (vertical bar r vertical bar approximate to 0.75), while index values during the vegetative growth period were not correlated with yield and fruit quality. In general, the inclusion of remote sensing data throughout the growing seasons enabled horticulturists to improve fruit yield and quality estimation and schedule production processes.