Multimodal sentiment analysis is an information processing technique that attempts to predict human emotional states from multiple modalities like text, audio, and video. Due to challenges in aligning multiple modalities, existing methods are limited to analysis at course or fine granularity, which risks missing nuances in human emotional expression. Researchers have now developed an innovative approach to MSA that reduces computational time required to sentiment prediction while offering improved performance.

Recently, a team led by Professor Weifeng Zhang and Peng Ning from the College of Resources and Environmental Sciences at China Agricultural University proposed a sustainable production pathway to achieve an annual yield of 22.5 t·ha^–1 in the winter wheat-summer maize rotation system on the North China Plain, providing a scientific reference for solving this problem. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025618).

Xusheng Meng and colleagues from Nanjing Agricultural University proposed a green, high-yield, and high-efficiency rice technology system in a review study, providing a solution to this problem. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025636).

Recently, a research team led by Professor Zhaohui Wang from the College of Natural Resources and Environment at Northwest A&F University proposed a technical framework for green wheat production and a regionally adapted model, providing ideas to solve this problem. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025606).