Objective:To achieve rapid and accurate detection of several common strawberry diseases and nutrient deficiencies in a facility agricultural planting environment ,a target detection method based on an improved YOLOv5 model was proposed.Methods:By replacing the C2f module with the C3 module , adding the SimAM attention mechanism , and adding detection layers , the original YOLOv5 model structure was improved.The leaves ,flowers ,and fruits of strawberry plants were studied ,and the improved YOLOv5 model was applied to extract growth characteristics of strawberry plants and quickly detect diseases and nutrient conditions.Results:The improved YOLOv5 model had a high accuracy in identifying the growth status of straw berry plants , and the average detection accuracy of each recognition item averaged (mAP@0.5) 97.9％,which was 13.6％ and 10.3％ higher than the original YOLOv5 and YOLOv8 models , respectively.The recognition image transmission frame rate was improved by 22 f/s compared to the original YOLOv5 model.Conclusion:This model could realize the identification and detection of straw berry diseases and nutrient status under the facility agricultural environment ,with high recognition accuracy and fast recognition speed ,and was suitable for mobile chassis deployment , providing a certain technical support for intelligent and accurate spray of straw berries.
维普
万方数据