Abstract: Aiming at the problem of mildew during the storage of tobacco leaves, the traditional prevention and control measures are not effective, and the existing tobacco leaf mildew prediction model has low accuracy, which cannot effectively reduce the occurrence of tobacco leaf mildew. In order to improve the accuracy of tobacco leaf mildew state prediction, a method based on one-dimensional convolution deep neural network (1D-CNN) is proposed. Based on the collection of terminal sensor data, it is stan- dardized and processed to obtain the model's training features. A 1D-CNN is trained to predict the mildew state of tobacco leaves, and the network structure is optimized. The experimental results show that the proposed method has higher prediction accuracy than other traditional models. Finally, an intelligent monitoring system for tobacco leaf storage mildew is designed and implemented to realize the real-time prediction function of tobacco leaf mildew, and good results are achieved.

Key words: tobacco leaf mildew, convolutional neural network, mildew prediction