A convolutional neural network approach to detecting fruit physiological disorders and maturity in ‘Abbé Fétel’ pears

Abstract

Image processing through the implementation of manually coded algorithms has been adopted to detect fruit damage during post-harvest operations. This study tested convolution neural networks with “You Only Look Once” (YOLO) architecture using a commercial online platform to detect physiological disorders and ripening stage in ‘Abbé Fétel’ pear. Disorders such as superficial scald and the starch pattern index (SPI) were assessed. Three different models were trained to detect: I) individual fruit within the boxes; II) superficial scald or senescence scald on pear skin; III) the SPI value of pears was assessed using the Lugol solution. Preliminary statistics show that the model to count the fruit inside the boxes reached 64.70% of true positives with 0.5 intersection over union. The second had less accuracy (up to 20% of true positives) but maintained a good average precision (60%) with different confidence thresholds (40% and 20%). The third showed good performances compared to the Ctifl and Laimburg scales, with an F1 score of 0.36 and 0.59, respectively. The effectiveness of the transfer learning method was demonstrated. However, further image labelling and modelling research is needed to improve the accuracy of the simulations and to develop an application for portable devices for pre- and post-harvest factor mapping. These results could lead to improvements in the management of fruit boxes and thus help ensure good fruit quality for consumers. • Artificial intelligence allows to count fruit, to detect disorders and starch index. • Model for pear detection to estimate number of fruit gets F1 score of 0.84 • Model for superficial scald to detect early storage disorders gets an F1 score of 0.28 • Model for starch index to evaluate fruit ripening gets an F1 score of 0.59 • Neural networks applied to food production chain ensure good quality for consumers.