An improved YOLOv8 apple leaf disease detection algorithm

Abstract

This paper suggests an improved you only look once version 8n (YOLOv8n) algorithm for apple leaf disease detection, abbreviated as ALWB-YOLOv8n. The model is comprised of four essential components. Initially, arbitrary kernel convolution (AKConv) replaces the convolution module, which significantly decreases both the model’s parameter count and its overall size. Secondly, the large selective kernel network (LSKNet) attention mechanism is added in the Backbone, which can dynamically adjust the spatial sensory domain, and experiments have proved that this method is extremely advantageous for small target detection. Third, a weighted bi-directional feature pyramid network is introduced, which enables the model to achieve multi-scale feature fusion and is more concise and faster. Finally, wise intersection over union (WIoU) is used to replace complete intersection over union (CIoU) in YOLOv8, and the idea of focal loss is introduced, which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves. The improved algorithm exhibits superior performance compared to other common object detection algorithms. Compared with YOLOv8n, the improved algorithm achieves 2.3% improvement in precision, 3.8% improvement in recall, and 2.5% and 2.7% improvement in mAP0.5 and mAP0.5: 0.95, respectively. Compared with YOLOv8n, the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second (FPS) of 50.5.

Keywords

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References

[1] Alqethami S, Almtanni B, Alzhrani W, et al.. Disease detection in apple leaves using image processing techniques. Engineering, technology & applied science research, 2022, 12(2): 8335-8341 J]

[2] Sagar S, Singh J. An experimental study of tomato viral leaf diseases detection using machine learning classification techniques. Bulletin of electrical engineering and informatics, 2023, 12(1): 451-461 J]

[3] Zhang N, Zhang X, Shang P, et al.. Detection of cotton verticillium wilt disease severity based on hyperspectrum and GWO-SVM. Remote sensing, 2023, 15(13): 3373-3393 J]

[4] Bachhal P, Kukreja V, Ahuja S, et al.. Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique. Scientific reports, 2024, 14(1): 10219-10239 J]

[5] Sharifani K, Amini M. Machine learning and deep learning: a review of methods and applications. World information technology and engineering journal, 2023, 10(07): 3897-3904[J]

[6] Girshick R, Donahue J, Darrell T, et al.. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23–28, 2014, Columbus, OH, USA, 2014New YorkIEEE580-587[C]

[7] Girshick R. Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, December 13–16, 2015, Santiago, Chile, 2015New YorkIEEE1440-1448[C]

[8] Ren S, He K, Girshick R, et al.. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1137-1149 J]

[9] Gong X, Zhang S. A high-precision detection method of apple leaf diseases using improved faster R-CNN. Agriculture, 2023, 13(2): 240-264 J]

[10] Cong P, Li S, Zhou J, et al.. Research on instance segmentation algorithm of greenhouse sweet pepper detection based on improved mask RCNN. Agronomy, 2023, 13(1): 196-219 J]

[11] Kang S H, Park J S. Aligned matching: improving small object detection in SSD. Sensors, 2023, 23(5): 2589-2603 J]

[12] Xu C, Dong Z, Zhong S, et al.. Fusion network for small target detection based on YOLO and attention mechanism. Optoelectronics letters, 2024, 20(6): 372-378 J]

[13] Wang Y, Xing Z, Ma L, et al.. Object detection algorithm for Lingwu Long Jujubes based on the improved SSD. Agriculture, 2022, 12(9): 1456-1472 J]

[14] Chen Z, Ww R, Lin Y, et al.. Plant disease recognition model based on improved YOLOv5. Agronomy, 2022, 12(2): 365-398 J]

[15] Yao J, Qi J, Zhang J, et al.. A real-time detection algorithm for Kiwifruit defects based on YOLOv5. Electronics, 2021, 10(14): 1711-1723 J]

[16] Zhang L, Ding G, Li C, et al.. DCF-YOLOv8: an improved algorithm for aggregating low-level features to detect agricultural pests and diseases. Agronomy, 2023, 13(8): 2012-2027 J]

[17] Chen J, Ma A, Huang L, et al.. GA-YOLO: a lightweight YOLO model for dense and occluded grape target detection. Horticulturae, 2023, 9(4): 443-464 J]

[18] Zhong Z, Yun L, Cheng F, et al.. Light-YOLO: a lightweight and efficient YOLO-based deep learning model for mango detection. Agriculture, 2024, 14(1): 140-159 J]

[19] Yang G, Wang J, Nie Z, et al.. A lightweight YOLOv8 tomato detection algorithm combining feature enhancement and attention. Agronomy, 2023, 13(7): 1824-1837 J]

[20] Yang H, Deng X, Shen H, et al.. Disease detection and identification of rice leaf based on improved detection transformer. Agriculture, 2023, 13(7): 136-152 J]

[21] Li W, Zhu L, Liu J. PL-DINO: an improved transformer-based method for plant leaf disease detection. Agriculture, 2024, 14(5): 691-704 J]

[22] Lv M, Su W H. YOLOv5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection. Frontiers in plant science, 2024, 14: 1323301 J]

[23] Yang Q, Duan S, Wang L. Efficient identification of apple leaf diseases in the wild using convolutional neural networks. Agronomy, 2022, 12(11): 2784-2803 J]

[24] ZHANG X, SONG Y, SONG T, et al. AKConv: convolutional kernel with arbitrary sampled shapes and arbitrary number of parameters[EB/OL]. (2023-11-20) [2025-02-26]. https://arxiv.org/abs/2311.11587v2.

[25] TONG Z, CHEN Y, XU Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[EB/OL]. (2023-01-24) [2025-02-26]. https://arxiv.org/abs/2301.10051.

[26] Tan M, Pang R, Le Q V. EfficientDet: scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13–19, 2020, Seattle, WA, USA, 2020New YorkIEEE10781-10790[C]

[27] LI Y X, HOU Q B, ZHENG Z H, et al. Large selective kernel network for remote sensing object detection[EB/OL]. (2023-03-16) [2025-02-26]. https://arxiv.org/abs/2303.09030.

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