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Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model

التفاصيل البيبلوغرافية
العنوان: Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model
المؤلفون: Bo Zhao, Qifan Zhang, Yangchun Liu, Yongzhi Cui, Baixue Zhou
المصدر: Applied Sciences, Vol 14, Iss 6, p 2575 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: YOLOv8n, rice seeding planting conditions, deep learning, image processing, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: In response to the need for precision and intelligence in the assessment of transplanting machine operation quality, this study addresses challenges such as low accuracy and efficiency associated with manual observation and random field sampling for the evaluation of rice seedling planting conditions. Therefore, in order to build a seedling insertion condition detection system, this study proposes an approach based on the combination of image processing and deep learning. The image processing stage is primarily applied to seedling absence detection, utilizing the centroid detection method to obtain precise coordinates of missing seedlings with an accuracy of 93.7%. In the target recognition stage, an improved YOLOv8 Nano network model is introduced, leveraging deep learning algorithms to detect qualified and misplaced seedlings. This model incorporates ASPP (atrous spatial pyramid pooling) to enhance the network’s multiscale feature extraction capabilities, integrates SimAM (Simple, Parameter-free Attention Module) to improve the model’s ability to extract detailed seedling features, and introduces AFPN (Asymptotic Feature Pyramid Network) to facilitate direct interaction between non-adjacent hierarchical levels, thereby enhancing feature fusion efficiency. Experimental results demonstrate that the enhanced YOLOv8n model achieves precision (P), recall (R), and mean average precision (mAP) of 95.5%, 92.7%, and 95.2%, respectively. Compared to the original YOLOv8n model, the enhanced model shows improvements of 3.6%, 0.9%, and 1.7% in P, R, and mAP, respectively. This research provides data support for the efficiency and quality of transplanting machine operations, contributing to the further development and application of unmanned field management in subsequent rice seedling cultivation.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/14/6/2575; https://doaj.org/toc/2076-3417
DOI: 10.3390/app14062575
URL الوصول: https://doaj.org/article/c306dde76d874f0d8b691b057aa6b4d7
رقم الأكسشن: edsdoj.306dde76d874f0d8b691b057aa6b4d7
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app14062575