Comparative Analysis of Conventional and Deep Learning Algorithms for Apple Detection

Abstract

Abstract Precise apple counting is one of the vital tasks in the overall context of agricultural applications, from yield estimation to resource allocation and several other logistical issues about harvesting. The paper has discussed a comparison of conventional image processing techniques with modern deep learning for the detection of apples. Apple detection research, focusing on apples under difficult orchard situations using the MinneApple dataset to ensure strong deep learning-based detection due to YOLOv8, was studied. In particular, it compared traditional approaches using HSV color segmentation and morphological operations against a fine-tuned YOLOv8 model, improved by Roboflow. These studies proved the deep learning approach of difficult scenarios with very high precision, recall, and F1 score, thus enabling it to rightly distinguish apples on the trees from those lying on the ground. Results are highly useful in understanding how traditional and modern methods can be integrated into agricultural automation.

Code and data availability

The paper uses the publicly available MinneApple dataset, but that is a cited prior-work benchmark, not a paper-specific asset. No author code, trained model checkpoints (best.pt/last.pt), or data deposits are shared with any availability statement or URL.

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