Image recognition method based on internet of things and artificial intelligence technology - Hongwei Zhang (Systems and Soft Computing)
Fusarium has a significant impact on the growth process of wheat, affecting its yield and quality. This study proposes an image recognition method based on the Internet of Things and artificial intelligence technology to investigate the impact of wheat Fusarium on the growth process of wheat. By designing a convolutional neural network model specific to wheat Fusarium, this model can achieve accurate recognition of wheat Fusarium disease images. The model utilizes convolutional neural networks for image preprocessing and filtering, effectively extracting image features, and obtaining high-resolution semantic features through feature fusion. By combining IoT technology, rapid calculation and feedback of recognition accuracy have been achieved. The experimental results show that this method has high recognition accuracy and short recognition time in wheat Fusarium image recognition, providing a new means for predicting and controlling wheat diseases and pests. The application of this technology is expected to provide strong support for the food production security in spring wheat producing areas in China. This study utilizes the Internet of Things and artificial intelligence technology to segment wheat population images. Using a multi-channel convolutional neural network with a joint loss function to recognize segmented images of wheat Fusarium head blight. Compared with traditional image recognition methods, group normalization is more accurate than convolutional neural network models under batch normalization. Under the same conditions of improving the overall performance of the network, the accuracy of using group normalized convolutional neural networks is 7 % higher than that of batch normalized convolutional neural networks.