Identification of soybean leaf disease based on local features and visual bag of words model

Disease detection is crucial for improving soybean crop yields. In response to the low efficiency and accuracy of disease recognition and classification caused by traditional visual diagnosis methods for soybean crop diseases, a classification algorithm based on local descriptors and visual bag-of-words techniques was proposed to represent soybean leaf images, while preserving visual information about potential diseases. Four algorithms such as SIFT, DSIFT, PHOW, and SURF, were employed to classify and recognize soybean leaf diseases such as downy mildew, rust TAN, and rust RB. The results demonstrated that the local descriptor PHOW yielded the best classification and recognition results, with an accuracy rate of 96. 25%. Further research on the recognition effects of PHOW in different color spaces revealed that, compared to grayscale images, the use of HSV and Opponent color spaces could effectively improve the correct classification rate of soybean leaf disease detection, reaching accuracy rates of 99. 83% and 99. 58% respectively. This validates the feasibility and efficiency of identifying soybean leaf diseases by using local descriptors and visual bag-of-words techniques, and provides a general classification and recognition method for the disease recognition of other crops.