Work

A lightweight detection model for rice grain with dense bonding distribution based on YOLOv5s

Year: 2025

Type: article

Abstract: Accurate counting of rice grains plays a critical role in rice breeding and thousand-grain weight measurement. However, the accuracy of existing algorithms is insufficient under conditions of high-density and densely bonded rice grain distribution. To quickly and accurately detect high-density and densely bonded rice grains with as many grains as possible, this study developed a lightweight model based on YOLOv5s. First, we built an efficient lightweight model architecture to obtain small-target location and semantic information of rice grains. Second, we use an omni-dimensional dynamic convolution (ODConv) module to replace some of the convolutions of the backbone network to fully extract feature information. We then introduce the mixed local channel attention (MLCA) mechanism to weigh local features through spatial information, allowing the model to locate and identify dense rice grains accurately. Finally, we use the SIoU loss function to improve the convergence speed and accuracy of model training. The model’s detection accuracy was verified via ablation experiments. The results indicated that compared with the original YOLOv5s network, the model size, parameters and floating-point operations per second (FLOPS) of the improved model decreased by 64.16 %, 70.8 % and 28.3 %, respectively, while mAP 0.5:0.95 increased by 7.21 %. The mean error rate and mean detection time of the improved model were 0.234 % and 25.9 ms, respectively. Its superior capacity against other detection algorithm models at rapidly detecting densely bonded rice grains. Furthermore, an android application was further developed. After comparative testing on three types of mobile phones, the application was able to effectively and accurately detect and count rice grains, providing an effective solution for rice grain detection and counting.

Source: Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2025.110672

Authors: Dongquan Chen, Wenbin Sun, Kang Xu, Yiren Qing, Guiquan Zhou, Ranbing Yang

Institution: Hainan University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,870