A Lightweight YOLOv5 Target Detection Model and Its Application to the Measurement of 100-Kernel Weight of Corn Seeds

Abstract

The 100-kernel weight of corn seed is a crucial metric for assessing corn quality, and the current measurement means mostly involve manual counting of kernels followed by weighing on a balance, which is labour-intensive and time-consuming. Aiming to address the problem of low efficiency in measuring the 100-kernel weight of corn seeds, this study proposes a measurement method based on deep learning and machine vision. In this study, high-contrast camera technology was utilised to capture image data of corn seeds. And improvements were made to the feature extraction network of the YOLOv5 model by incorporating the MobileNetV3 network structure. The novel model employs deep separable convolution to decrease parameters and computational load. It incorporates a linear bottleneck and inverted residual structure to enhance efficiency. It introduces an SE attention mechanism for direct learning of channel number features and updates the activation function. Algorithms and experiments were subsequently designed to calculate the 100-grain weight in conjunction with the output of the model. The outcomes revealed that the enhanced model in this study achieved an accuracy of 90.1%, a recall rate of 91.3%, and a mAP (mean average precision) value of 92.2%. While meeting production requirements, this model significantly reduces the number of parameters compared to alternative models—50% of the original model. In an applied study focused on measuring the 100-kernel weight of corn seeds, the counting accuracy yielded a remarkable 97.18%, while the accuracy for weight measurement results reached 94.2%. This study achieves both efficient and precise measurement of the 100-kernel weight of maize seeds, presenting a novel perspective in the exploration of maize seed weight.