In order to solve the problem that the gray level distribution of crop canopy thermal infrared image isuneven and has large noise, the traditional image segmentation method is difficult to realize the effective recognition of its target region. In this study, by the thermal infrared images of adzuki bean canopy’s in the seedling stage was taken as the research object, Combining fuzzy neural network and affine transformation, a crop canopy recognition model based on thermal infrared image processing technology was proposed. First, the adaptive characteristics of the five-layer linear normalized fuzzy neural network were used to select the Gaussian membership function to automatically calculate the inference rules for canopy visible light image recognition, effectively segmenting the canopy area in the visible light image. By analyzing three segmentation indexes and entropy, the canopy segmentation quality of visible light images was quantitatively evaluated. When the network iterates 38 times, the error precision was 0.000 952, and the visible light image of the crop canopy was obtained. The average effective partition rate of the algorithm was 96.13%, and the entropy value of the image source average information was 2.454 4~5.198, which was only 0.245 9 different from the entropy of the canopy image obtained by the standard algorithm. Then, using the effective area of the canopy to obtain the visible light image as a reference image, the affine transformation algorithm was used to adjust the image transformation factors such as optimal translation, rotation, and scaling. To register the raw thermal infrared image. And a canopy thermal infrared image recognition method based on affine transformation was proposed. For a crop thermal infrared image with an initial temperature range of 16.35~19.92, when the rotation amplitude was 1.0 and the zoom factor was 0.9, the maximum temperature difference of the target image obtained as the optimal registration parameter of the heterogeneous image was 3.17 ℃. Relative The average temperature of the original image decreased from 18.711 ℃ to 17.790 ℃, and the crop canopy recognition based on thermal infrared image processing technology was realized. Finally, mutual information was used as a monitoring index to evaluate the thermal infrared image recognition method of crop canopy. In the canopy thermal infrared image recognition method proposed in this study, the average mutual information between the acquired target image and the initial thermal infrared image was 4.368 7, while the average mutual information between the standard target image and the initial thermal infrared image was 3.981 8, and the difference between the two was only 0.486 9. At the same time, the average temperature difference between the two canopy thermal infrared images was 0.25 ℃, which effectively eliminates the background noise of the original thermal infrared images. The research results show that the effectiveness and practicability of this research method could provide a technical reference for the application of thermal infrared images to reflect the characteristic parameters of crop physiological and ecological information.