Abstract:Weed control in paddy fields is a key agronomic measure to improve rice yield, and chemical weed control is widely used because of its high efficiency. Traditional chemical weed control relies on manual operation and often uses large area spraying, which increases the operation cost and causes negative problems such as environmental pollution. Based on this background, a precision spraying type paddy weeder for adaptive weeding operation was designed. The weeder spraying device and system were constructed, and the weed detection system with MS YOLO v7 as the core framework was designed based on the constructed diversified paddy field weed dataset. The MS YOLO v7 model combined the backbone network with MobileOne, and replaced the CIoU loss function with the SIoU loss function. The model performance was verified by ablation test and different model comparison test, and the results showed that the model recognition accuracy was 95.65% , the mean average precision ( mAP) was 92.67% , and the real-time performance reached 51.29 f / s. The IR model was reasoned by using OpenVINO on Raspberry Pi, and the detection of a single paddy field weed image took 0.806 s. The constructed spraying system can instantly capture and analyze the transmission signals from the weed detection system, and then realize the precise regulation of the weed spraying device. The results of the field test showed that the precision spraying type paddy field adaptive weeder had a seedling injury rate of 2.95% , a target application accuracy of 94.98% , and a coefficient of variation of 0.128% , which met the agronomic requirements for weed control in paddy fields. The weeder realized the unmanned operation of paddy field weeding and it can provide technical reference for the intelligent development of agriculture.
