Detection of crop disease leaf based on multi-modal feature alignment
Aiming at the problem that the existing methods of crop disease leaf detection were not accurate enough to locate the leaf disease region by using image features,a new method of crop disease leaf detection based on multi-modal feature alignment was proposed.During the training phase,image and text from a collection of crop leaves were first encoded using visual and text encoders.The diseased areas in a given image were located according to the visual encoding features,and the integration of visual and text encoding features was used to achieve fine-grained classification of the type of disease in the diseased area.In the inference phase,the pretrained disease area localization module was used to locate the diseased areas in a given test image,and the extracted diseased areas were used as input for a pretrained classification model.Finally,by calculating the similarity between the predicted text values and the original labels in the text set,a rapid fine-grained classification result for the diseased area was obtained.Tests on several open-source crop disease datasets show that the proposed method can achieve high precision rates of 0.957 4,0.961 1,0.958 0,and 0.950 2 on potato,tomato,apple,and strawberry datasets,respectively.It has better comprehensive perfor mance and good paratical application value.
NETL
NSTL
万方数据
