The international research trend of tomato molecular breeding was studied based on thesis analysis in SCIE. Through the analysis of the annual number of articles, reveals the variation trend of related research. By the analysis of Scientific productivity and achievement influence, compares the research competitiveness among countries. Use the co-word clustering to identify 6 important research topics in the field, and use the strategy coordinates to evaluation the relative position of these topics .The result shows that, tomato molecular breeding research presents a rising trend. The United States plays an absolute leading role in the field. China’s research layouts more but achievements influence is relatively low. # 5 (tomato genome) and # 1 (resistance) themes are the center of tomatoes molecular breeding. # 2 (oxidative stress) may be the center of the future research.

In order to promote the construction of agricultural information service standards and standard system, and make the agricultural information service industry to develop orderly, we analyzed the inherent logic relation between agricultural information service, agricultural information service standards and the standard system based on the description of related concepts. In the development process of agricultural information service, we find that the development of agricultural information service is the basis for standard setting, the standards of agricultural information service are components of the standard system, agricultural information service standard system can guide the standard setting, and the agricultural information service standards can standardize and enhance the development and progress of the agricultural information service industry. In short, the agricultural information service, agricultural information service standard and the standard system interrelate, cannot be separated, and jointly promote the scientific, sustainable and healthy development of agricultural information service. This study established the foundation for the research of agricultural information service standard and standard system.

Due to the reliance of deep learning on high-quality datasets and the uncertainty regarding the reproductive period segmentation and image annotation of the Pleurotus citrinopileatus maturity dataset, this study aims to enhance the accuracy of dataset annotation by leveraging the color change patterns of Pleurotus citrinopileatus. First, reproductive period division and color feature analysis were conducted. Specifically, considering the influence of the cultivation environment, a Pleurotus citrinopileatus color feature database was constructed using data from different environmental batches. Second, the growth process of Pleurotus citrinopileatus fruiting bodies was divided into four stages: primordium stage, young mushroom stage, forming stage, and maturation stage. Third, color pattern verification revealed that through HSV color space analysis, the main hue exhibited periodic changes. Experimental data showed that when the main hue exceeded 28°, it entered the next growth stage, decreasing to 23° before rising again, which validated the division of the four reproductive periods from primordium to maturity. Next, feature extraction and model construction were performed. Statistical measures such as mean, variance, and peak values of the H, S, and V channels for each sample were calculated to build a Gradient Boosting Tree (GBT) classification model based on color features. A new dataset was generated by adjusting expert annotations based on model validation. Finally, the experimental results of dataset quality were annotated and performance improvements were verified. Using the YOLO12 model for image feature learning of Pleurotus citrinopileatus, training and testing were conducted with both the original and improved datasets. The improved dataset(0.935 mAP@0.5 vs. 0.91 mAP@0.5), representing a 2.5 percentage point increase. On a test set of 417 images, the accuracy of the improved dataset reached 94.5%, compared to 92% for the original dataset, marking a 2.5 percentage point improvement. In conclusion, analyzing the color features of Pleurotus citrinopileatus effectively guides the division of maturity datasets, thereby enhancing the performance of deep learning-based Pleurotus citrinopileatus maturity recognition models.

To the problems of the current agricultural Q&A system: poor user experience, low precision, and not fully utilized resources, the multi-level association question answering system is developed based on agricultural knowledge map, and Jaccard index and Vector space mode. The application proves that the user experience is good, and the retrieval precision efficiency is improved effectively.

Along with the popularity enhancement of the mobile phone, lots of farmers access to agricultural service by it. By investigation status in Beijing suburb, the result shows that: The conditions of using mobile phones to carry out agricultural science and technology information services mature gradually. In addition, there are many ways to carry out service, and should be based on public welfare. Meanwhile, there are some difficulties to make greater achievements. Finally, some proposal is put forward in this paper. It provides some useful references for the farmers to carry out the service by using the new channel.