Weed recognition in vegetable at seedling stage based on deep learning and image processing

In this study，the recognition test of bok choy and its associated weeds at the seedling stage was carried out，and a novel method based on recognizing vegetables and then indirectly recognizing weeds was proposed. By combining deep learning and image processing technology，this method can effectively reduce the complexity of weed recognition，and at the same time improving the accuracy and robustness of weed recognition. First，a neural network model was used for detecting the bok choy and drawing bounding boxes. The green targets outside the bok choy bounding boxes were marked as weeds，and color features were used to segment them. Besides，an area filter was used for eliminating the noises and extracting weed regions. In order to explore the effects of different deep learning models on bok choy recognition，SSD model，RetinaNet model and FCOS model were selected，and three evaluation metrics of F1 value，average accuracy and detection speed were used for comparative analysis. The SSD model was the best model for bok choy recognition，with the highest detection speed and excellent recognition rate. Its F1 value，average accuracy and detection speed in the test set were 95.4%，98.1% and 31.0 f/s，respectively. The improved MExG index can effectively recognize weeds，and the segmented weeds have complete shapes and clear outlines. Experiment results show that the proposed method for recognizing weeds in vegetable fields is highly feasible and has excellent application effects，which can also provide technical reference for weed recognition in similar crop fields. © 2023 Editorial Board of Jilin University. All rights reserved.