Recently, deep learning methods are widely used in the rice diseases identification. However, the actual image background of rice disease is complex, the classification performance is not ideal. Therefore, this paper proposed a multi-scale feature extraction method based on stacked autoencoder, named the multi-scale stacked autoencoder (MSSAE), to improve the recognition accuracy of rice diseases. This method extracts the complex rice disease image’s features by two steps. In the first step, the images are preprocessed. Then, the MSSAE extract the multi-scale features through preprocessed rice diseases data in different scales. Through comparative analysis of experiments, the new method achieved greater than 95% precision in the detection of rice diseases. It indicated that the MSSAE model has an outstanding identification performance for actual crop disease image recognition.

In recent years, the price of small agricultural products has both plummeted and skyrocketed, which has a great impact on people’s lives. Studying the factors affecting the price fluctuation of small agricultural products is of great significance for stabilizing their price. With the development and application of social media, farmers and consumers are more greatly influenced by online public opinion, resulting in irrational planting behavior or purchasing behavior, which has a complex impact on the price of small agricultural products. Taking garlic as an example, we crawled through network public opinions about garlic price from January 2015 to December 2020 using web crawler technology. Then, the network public opinions were quantified using a natural language processing and time-varying parameter vector autoregression (NLP-TVP-VAR) model to empirically analyze their dynamic influence on garlic price fluctuation. It was found that both public attitude and public attention have a short-term influence on garlic price fluctuation, and the influences of each differ according to direction, intensity and timing. The influence of public attitude on garlic price fluctuation is positive, while the influence of public attention on garlic price fluctuation is largely negative. The influence intensity of public attitude is stronger than of public attention on garlic price fluctuation. The influence of public attitude on garlic price fluctuation shows a trend of intensifying, while that of public attention has been weaker than in previous years. In addition, based on the results of our study, we present some recommendations for improving the comprehensive information platform and price fluctuation early warning system for the whole industry chain of small agricultural products.

The precision spraying of herbicides can significantly reduce herbicide use, and recognizing different field weeds is an important part of it. In order to enhance the efficiency and accuracy of field weed recognition, this article proposed a field weed recognition algorithm based on VGG model called VGG Inception (VGGI). In this article, three optimizations were made. First, the reduced number of convolution layers to reduce parameters of network. Then, the Inception structure was added, which can maintain the main features, and have better classification accuracy. Finally, data augmentation and transfer learning methods were used to prevent the problem of over-fitting, and further enhance the field weed recognition effect. The Kaggle Images dataset was used in the experiment. This work achieved greater than 98% precision in the detection of field weeds. In actual field, the accuracy could reach 80%. It indicated that the VGGI model has an outstanding identification performance for seedling, and has significant potential for actual field weed recognition.