Seed distribution information detection is an essential prerequisite for ensuring the performance of seeders，providing primary data for precise and efficient management of crop growth processes such as seed quality detection，depth determination and fertilization，field weeding，and harvesting. Computer vision technology has been widely applied in multiple fields by extracting meaningful information from image and video data and analyzing and explaining physical phenomena. Based on a systematic analysis of existing literature，this article explored the research progress of computer vision technology in seed distribution information detection from the perspectives of technology application scenarios，hardware devices，software processing algorithms，etc. The study found that computer vision technology still faces challenges in accuracy and stability in seed distribution information detection，and seed distribution information detection requires real-time and large-scale data processing capabilities. Traditional algorithms and devices are difficult to meet these needs，and data fusion and analysis capabilities still need to be improved. Accurate interpretation of seed distribution information requires comprehensive utilization of multiple sensor data and effective data processing and analysis to obtain more comprehensive and accurate results. In response to the above issues，we proposed the following suggestions：Firstly，the widespread application of deep learning and machine learning algorithms is crucial. By utilizing large-scale annotated data for model training，the accuracy and robustness of the algorithm can be improved；Secondly，it is necessary to improve the adaptability of the algorithm and optimize it for different soil conditions，seed types，and light changes to ensure that the algorithm can work effectively in various environments； At the same time，developing multi-sensor data fusion technology can provide more comprehensive and accurate seed distribution information by fusing image data with other sensor data，such as laser scanning and thermal infrared images. This study aimed to provide a reference for the future development of computer vision in seed distribution information detection.

To solve the problem of field waterlogging during wheat sowing in rice-wheat rotation areas, which causes sticky and wet soil, thus affecting the growth of wheat, this paper proposed a sloped ditching method based on laser alignment technology, and designed a combined ditching device with a front ditching shovel (FDS) and a rear ditching plow (RDP) to create sloped drainage furrows when sowing wheat. The key factors affecting the performance of RDP and value ranges were determined through theoretical analysis. Through discrete element method (DEM) simulation, the influence of different structural parameter combinations on slope stability was studied, and the optimal parameter combination was determined as the soil lifting angle of 50°, the minimum element angle of 35°, and the maximum element angle of 40°. The field test showed that the ditching device can effectively create sloped trapezoidal drainage furrows. The slope stability coefficient and slope accuracy coefficient were both greater than 85%, which meets the requirements of drainage. This paper provides a new ditching method and theoretical basis for the development of a sowing and ditching combined machine in rice stubble fields.