Inicio  /  Applied Sciences  /  Vol: 14 Par: 6 (2024)  /  Artículo
ARTÍCULO
TITULO

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

Bo Zhao    
Qifan Zhang    
Yangchun Liu    
Yongzhi Cui and Baixue Zhou    

Resumen

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.

 Artículos similares

       
 
Mohamed Shenify, Fokrul Alom Mazarbhuiya and A. S. Wungreiphi    
There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT d... ver más
Revista: Applied Sciences

 
Shancheng Tang, Ying Zhang, Zicheng Jin, Jianhui Lu, Heng Li and Jiqing Yang    
The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, t... ver más
Revista: Applied Sciences

 
Rong Wang, Xinyang Zhou, Yi Liu, Dongqi Liu, Yu Lu and Miao Su    
To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResN... ver más
Revista: Applied Sciences

 
Ru Ye, Hongyan Xing and Xing Zhou    
Addressing the limitations of manually extracting features from small maritime target signals, this paper explores Markov transition fields and convolutional neural networks, proposing a detection method for small targets based on an improved Markov tran... ver más

 
Linhua Zhang, Ning Xiong, Wuyang Gao and Peng Wu    
With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer... ver más
Revista: Information