We conducted a large‐scale, high‐throughput phenotyping analysis of the effects of various preharvest and postharvest features on the quality of ‘Valencia’ oranges in order to develop shelf‐life prediction models. Altogether, we evaluated 10,800 oranges (~3.6 tons) harvested from three orchards at different periods and conducted 151,200 measurements of 14 quality parameters. The storage time was the most important feature affecting fruit quality, followed by the yield, storage temperature, humidity, and harvest time. The storage time and temperature features significantly affected (p < 0.001) all or most of the tested quality parameters, whereas the harvest time, yield, and humidity conditions significantly affected several particular quality parameters, and the selection of rootstocks had no significant effect at all. Five regression models were evaluated for their ability to predict fruit quality based on preharvest and postharvest features. Non‐linear Support Vector Regression (SVR) combined with a data‐balancing approach was found to be the most effective approach. It allowed the prediction of fruit‐acceptance scores among the full data set, with a root mean square error (RMSE) of 0.195 and an R2 of 0.884. The obtained data and models should assist in determining the potential storage times of different batches of fruit.
- intelligent logistics
All Science Journal Classification (ASJC) codes
- Plant Science