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The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

In this paper, we used association recommendation to achieve page recommended for Beijing 12396 agricultural information networks, and improved accuracy, algorithm performance and execution efficiency of the recommendation. The contrast experiments proved that these improvements optimized the effect of recommendation from different aspects. So it can provide users with better agricultural information service.

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This paper achieved association recommendation in the Beijing 12396 Agricultural Information Network, and improved the recommendation from three aspects. The contrast experiments proved that the improvement optimized the effect of recommendation. An intelligent recommendation based on farmers' personalization features was also proposed. All these efforts were able to provide better agricultural information service for farmers.