Plant Remote Sensing Image Recognition and Landscape Design Based on Improved Res Net50

Using remote sensing technology to obtain and identify plant images can enhance data for landscape design and streamline the design process. To address the issue of low recognition accuracy in plant remote sensing models, this study developed an adaptive threshold binary mask algorithm using mixed maximum inter-class variance and an image classification algorithm based on an improved ResNet50 network, combining them into a plant remote sensing image recognition model. The test results showed that the average recognition recall and F1 mean of the designed model on various plant remote sensing images in the test set were 97.8% and 97.7%, respectively, which were 4.2% and 4.1% higher than the method ranked second in overall numerical values. The offline area of the receiver operation characteristic curve of the designed model on the test set was 73.5%, which was 8.4% higher than the algorithm before improvement. From the test results, the recognition model designed this time has stronger recognition ability than the currently commonly used models. This model can be used to assist landscape design, helping designers identify the distribution of plants in the landscape location and the survival status of green plants after construction.