Application of non-destructive sensors and big data analysis to predict physiological storage disorders and fruit firmness in ‘Braeburn’ apples

Abstract

Physiological storage disorders affect a range of commercially important pomefruit and result in fruit losses and wastage of resources. Disorders can develop during and/or after storage and symptoms are strongly influenced by the growing environment and orchard management. Furthermore, fruit which receive similar orchard management and storage can vary greatly in disorder incidence and severity. Biological systems are complex and simple cause-and-effect approaches have not up until now resulted in robust methods to predict disorder risk. Reliable predictions are needed by fruit industries worldwide to better manage fruit production processes, to determine optimal harvest dates and long-term storage regimes. The current work proposes a new methodological approach to model ‘Braeburn’ apple disorder risk. Autoregressive time series (ARX) models via model identification techniques for chlorophyll, anthocyanins, soluble solids and dry matter content were obtained from weather conditions and different orchard management treatments and then served as input into a classifier for internal browning, cavities and fruit firmness after long-term controlled atmosphere storage. The classification results for internal browning disorder show a 90% agreement between two separate years and for fruit firmness an 80% success rate was obtained by training the classifier with two years of data.