Apple Flower Recognition Using Convolutional Neural Networks with Transfer Learning and Data Augmentation Technique

Abstract

Automated monitoring of apple flowers using convolutional neural networks will enable informed decision-making for planning thinning and fruit set operations, optimizing crop load, preventing fruiting periodicity, and enhancing crop quality. The article presents the results of apple flower recognition quality on images using the YOLOv8 (You Only Look Once version 8) convolutional neural network model with the application of transfer learning and data augmentation technique. Pre-trained weights on the Common Objects in Context (COCO) dataset were utilized in the research. To expand the dataset and enhance model performance, the tools Flip, 90° Rotate, Crop, Rotation, Shear, Grayscale, Hue, Saturation, Brightness, Exposure, Blur, Noise, and Cutout were applied. The result showed that artificial augmentation of the training dataset significantly improves the quality of training for the YOLOv8 convolutional neural network model, increasing the average accuracy of detecting class features apple flowers. The analysis of the Precision-Recall curve allowed establishing a classification threshold (0.47) that provides the optimal balance between precision and recall in recognizing apple flowers at the flowering stage in images. The mAP metric for recognizing the «flower» class (flowers in the flowering stage) was 0.595. The analysis of the obtained results revealed an increase in the Precision metric by 2.1%, Recall metric by 10.13%, and mAP@0.5 metric by 5.31% when using the augmentation technique. The obtained results indicate a significant improvement in the performance of the model in recognizing apple flowers when applying the augmentation technique to the training dataset.