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.

With the increase in labor costs, using intelligent modern equipment to replace humans to complete a series of repetitive, boring, and even high-intensity labor is an important direction in current robot research. Sorting robots, as an important branch of agricultural robots, have good development potential. In order to replace manual, this article used the STM32 microcontroller, OpenMV machine vision module, and ESP32 microcontroller to design an intelligent fruit sorting robot so as to identify and sort fruits with different maturities and sizes. The OpenMV machine vision module collected the color and size information of the fruit, and judged whether the fruit was ripe. The STM32 microcontroller controlled the robot's movement and the robotic arm executed the corresponding sorting program to sort the fruit into the designated container. The ESP32 microcontroller uploaded the sorting data to the cloud. The robot used 3D software for structural design, established 3D models, and conducted simulation analysis during the design process. The simulation results indicated that sorting automation, informatization, and efficiency can be achieved to a certain extent. In the research, multiple and two sorting points were debugged on the physical object, and a total of 24 fruits were sorted. The success rate of fruit sorting reached 87.5%. The research results showed that the sorting robot can achieve precise sorting and achieve a certain degree of reliability and practicality, which can replace human beings to complete the work of precise fruit sorting.