Forecasting the potential of apple fruitlet drop by in-situ Vis-NIR spectroscopy

Apple trees (Malus domestica Borkh.) tend to exhibit a biennial cycle: a heavy-flowering year with an excessive amount of low-quality fruits is followed by a year with scarce flowering and low fruit load. Chemical thinning is currently the only viable solution in large commercial operations to ensure adequate yield. However, most thinners are effective only in the first few weeks following bloom and thinning efficiency depends on numerous factors and is difficult to predict. Forecasting the expected fruitlet drop after an initial thinner application would help perform corrections with the subsequent application. In this study, we used in-situ spectroscopy in the visible and near-infrared (Vis-NIR) range to forecast fruitlets drop rate. The study was carried out on “Golden Delicious” apple trees during two growing seasons – in April 2017 and April 2018. As commonly done in commercial orchards, the fruitlet drop was amplified by the application of synthetic auxins 1-naphthaleneacetic acid (NAA) and its amide (NAD). Fruitlets were tagged and monitored in situ every 2–4 days by measuring reflectance over the 400–1000 nm range. Special care was taken to assess sunlight interference during the measurement and correct it using a custom post-processing procedure. Measurements at 4–12 days after NAA/NAD treatment (days after treatment - DAT) were used to forecast fruitlet drop by 20–26 DAT (prediction dates). Principal component analysis (PCA(was carried out, followed by a classification algorithm (linear or quadratic discriminant analysis). Performing measurements on 4 DAT proved too early to predict fruitlet drop with satisfactory reliability (forecast accuracy 65%). Measurements at 6–12 DAT resulted in forecast accuracies of 80–97%, depending on the selected dates. The method offers a non-destructive prediction of apple fruitlet drop rate, which could lead to the development of a low-cost device that could help manage chemical thinning.