Neural network models of apple fruit identification in tree crowns: comparative analysis

Abstract

The article presents the results of an analysis conducted from 2022 to 2023 to assess the quality of modern neural network models of apple fruit identification in tree crowns shown in images. In order to conduct the studies on identifying the best detector, the following neural networks were used: SSD (Single Shot MultiBox Detector), YOLOv4 (You Only Look Once, Version 4), YOLOv5, YOLOv7, and YOLOv8. The performance of the considered models of apple fruit identification was assessed using such binary classification metrics as precision, recall, accuracy, F-score, and AUC-ROCTotal (area under the curve). To assess the accuracy in predicting apple fruit identification, the mean absolute percentage error (MAPE) of the analyzed neural network models was calculated. The neural network performance analysis used 300 photographs taken at an apple garden. The conducted studies revealed that the SSD model provides lower speed and accuracy, as well as having high requirements for computing resources, which may limit its use in lower performance devices. The YOLOv4 model surpasses the YOLOv5 model in terms of accuracy by 10.2 %, yet the processing speed of the YOLOv5 model is over twice that of the YOLOv4 model. This fact makes the YOLOv5 model preferable for tasks related to real-time big data processing. The YOLOv8 model is superior to the YOLOv7 model in terms of speed (by 37.3 %); however, the accuracy of the YOLOv7 model is 9.4 % higher. The highest area under the Precision-Recall curve amounts to 0.94 when using the YOLOv7 model. This fact suggests a high probability that the classifier can accurately distinguish between the positive and negative values of the apple fruit class. MAPE calculation for the analyzed neural network models showed that the lowest error in apple fruit identification amounted to 5.64 % for the YOLOv7 model as compared to the true value determined using the visual method. The performance analysis of modern neural network models shows that the YOLO family of neural networks provides high speed and accuracy of object detection, which allows them to operate in real time. The use of transfer learning (tuning of only the last layers to solve highly specialized problems) to adjust the performance of models for different apple fruit varieties can further improve the accuracy of apple fruit identification.