Crop seedling detection is an important task in the seedling stage of crops in fine agriculture. In this paper, we propose a high-precision lightweight object detection network model based on a multi-activation layer and depth-separable convolution module to detect crop seedlings, aiming to improve the accuracy of traditional artificial intelligence methods. Due to the insufficient dataset, various image enhancement methods are used in this paper. The dataset in this paper was collected from Shahe Town, Laizhou City, Yantai City, Shandong Province, China. Experimental results on this dataset show that the proposed method can effectively improve the seedling detection accuracy, with the F1 score and mAP reaching 0.95 and 0.89, respectively, which are the best values among the compared models. In order to verify the generalization performance of the model, we also conducted a validation on the maize seedling dataset, and experimental results verified the generalization performance of the model. In order to apply the proposed method to real agricultural scenarios, we encapsulated the proposed model in a Jetson logic board and built a smart hardware that can quickly detect seedlings.

Recent advancements in generative models have shown remarkable progress in music generation. However, most existing methods focus on generating monophonic or homophonic music, while the generation of polyphonic and multi-track music with rich attributes is still a challenging task. In this paper, we propose a novel approach for multi-track, multi-attribute symphonic music generation using the diffusion model. Specifically, we generate piano-roll representations with a diffusion model and map them to MIDI format for output. To capture rich attribute information, we introduce a color coding scheme to encode note sequences into color and position information that represents pitch,velocity, and instrument. This scheme enables a seamless mapping between discrete music sequences and continuous images. We also propose a post-processing method to optimize the generated scores for better performance. Experimental results show that our method outperforms state-of-the-art methods in terms of polyphonic music generation with rich attribute information compared to the figure methods.