No abstract

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

With the promotion of artificial intelligence in agriculture and the popularization of plug tray seedling-raising technology, seedling raising and transplanting have become the most popular planting modes. Miss-seeding is one of the most serious problems affecting seedling raising and transplanting. It not only affects the germination rate of seeds but also reduces the utilization rate of the plug tray. The experimental analysis of traditional machine vision-based miss-seeding showed that because of uneven lighting, the plug tray was wrongly identified as a seed under bright light, but the seeds in the dark were not easy to identify. When using the seeding area to identify seeds and noise, sweet corn seeds in a small area can be easily screened out. This paper proposes a method using the ResNet network with an attention mechanism to solve the above-mentioned problems. In this paper, the captured image was segmented into the images of a single plug tray, and a residual attention network was built; the detection scheme of miss-seeding was also converted into a dichotomous picture recognition task. This paper demonstrates that the residual attention network can effectively recognize and detect the seed images of sweet corn with very high accuracy. The results of the experiment showed that the average accuracy of this recognition model was 98%. The feature visualization method was used to analyze the features, further proving the effectiveness of the classification method of plug tray seedlings.