Identification of vegetable weeds by using convolutional neural networks and color segmentation
A wide variety and random distribution of weed species made weed identification challenging,resulting in low accuracy and poor real-time performance.In order to address this issue,a method based on deep convolutional neural networks for identifying spinach weeds was proposed.Initially,a deep convolutional neural network model was used to recognize spinach in segmented grid images,which helped in excluding grid images containing spinach.Subsequently,image processing techniques were applied to segment grid images without spinach,and background grid images lacking green pixels were identified,leaving the remaining grid images marked as weed images.Experimental results showed that the DenseNet model,RegNet model,and ShuffleNet model achieved overall spinach recognition accuracy above 0.965 on the test set,demonstrating excellent identification performance.Regarding recognition speed,the ShuffleNet model exhibited the highest computational efficiency,taking only 14.12 ms to recognize a single original image,corresponding to a frame rate of 70.84 fps,meeting the demands of real-time weed identification applications.By distinguishing spinach and differentiating weeds from soil,the complexity of weed identification was effectively reduced,while the weed identification accuracy was also enhanced.
万方数据
维普
