Detection and Severity Assessment of Potato Early Blight Ieaves in a Field Setting
The monitoring of potato disease degree is very important for the impact of potato yield,and the equipment for field disease monitoring needs to be deployed with lightweight models.In order to address issues such as limited hardware computing resources leading to the hard deployment and slow running speed of disease assessment networks in field devices,this paper proposes a real-time model to assess the severity of potato early blight disease in field scenarios.The model includes the early blight leaf recognition method based on a lightweight detector,and combines with the line counting via DeepSORT tracking algorithm,and a BP neural network for identifying the number of dis-eased and healthy leaves on the canopy level.Experiment shows that CS-YOLO reduces the parameter count by 90.03％compared to YOLOv5s in the task of early blight detection in potatoes,achieving an accuracy of 62.35％.The accuracy of line counting is found to be 0.686,which means early blight potato leaves could be effectively captured in detection and tracking tasks.The result also shows the BP neural network delivers a fitting result of predicting the disease severity,with a coefficient of determination R2 reaching up to 0.81.This method provides an effective detec-tion means for the deployment of agricultural protection robots in field scenarios,offering a viable solution for monito-ring and real-time assessment of potato early blight disease.
plant protectionpotato early blight diseaselightweighttarget trackingdisease assessment
万方数据
维普
