Integrating Soil, Leaf, Fruitlet, and Fruit Nutrients, Along with Fruit Quality, to Predict Post-Storage Quality of Staccato Sweet Cherries

Abstract

Predicting the post-storage quality of cherry fruits is crucial for determining their suitability for long-distance shipping or domestic distribution. This study aimed to forecast key quality attributes of Staccato sweet cherries after storage, simulating shipping conditions, by analyzing spring soil, leaf, fruitlet, and at-harvest data from thirty orchards in the Okanagan Valley, British Columbia, Canada, over two years. A support vector machine (SVM) was used to predict post-storage variables, with pre-harvest and at-harvest data selected by a genetic algorithm. The SVM accurately predicted soluble solids (R2 = 0.88), firmness (R2 = 0.83), and acidity (R2 = 0.79) after four weeks of storage, as well as visual disorders like slip skin and stem browning. Spring soil properties (Ca, Mg), leaf (N, Ca, Mg, Fe, Zn, B), and fruitlet data (N, Ca, Mg, B) were key predictors. Leaf Ca was vital for firmness and total soluble solids (TSS) prediction, while N in leaves and fruitlets influenced firmness, acidity, and disorders. Leaf Zn helped predict weight and acidity/TSS ratio, and Mg impacted fruit color. Pre-harvest leaf nutrition measured 3–4 weeks before harvest, proved most effective in predicting post-storage quality.

Keywords

• firmness
• total soluble solids
• nutrient composition
• support vector machine
• visual disorders