Weed Identification and Location for Crop at Seedling Stage Based on Enhancing and Fine-tuning Weather Data
Weeding is a crucial method for boosting crop yield,and the precise identification of weed species is imperative for effective weed control.In this study,the SXAU weed dataset was established and the weather data enhancement and online data augmentation were combined to enhance the weed feature extraction capability of Yolov8.This enhancement aimed to improve the model's practical application effectiveness while concurrently reducing the power consumption of computer hardware.Through the implementation of transfer learning,the Yolov8 model was initially pre-trained on a public weed dataset and subsequently fine-tuned using the SXAU weed dataset to swiftly acquire the relevant parameters needed by the neural network for weed feature recognition.Employing the Yolov8 algorithm in conjunction with the RealSense D435i depth camera,this approach involved utilizing a deep learning algorithm to derive the two-dimensional coordinates of crops and weeds within the image.Subsequently,the depth camera was employed to acquire three-dimensional coordinate information,facilitating spatial positioning of the crops and weeds.Experimental results demonstrate that,following the incorporation of weather data enhancement and fine-tuning,the model achieved an average detection accuracy of 97.43%and F1 value of 94.82%.The average detection time was 13.032 ms,surpassing performance metrics of Yolov7-tiny,Yolov5,Effcientdet,and other models.This research offered valuable insights for studies on lightweight weed identification,as well as the identification and spatial positioning of intelligent weeding robots.
weed feature extractionweather data enhancementtransfer learningYolov8lightweightdepth cemera
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