What has been the trajectory of water erosion model research in China? What are the most widely used models currently? What are the characteristics of research distribution across different regions? What shortcomings need urgent attention? Professor Qingfeng Zhang from Northwest A&F University, along with researchers from multiple institutions, systematically reviewed the research progress of water erosion models in China from 1982 to 2022 using a combination of bibliometric and statistical analysis methods, providing a panoramic perspective to answer these questions. Relevant study has been published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024580).

Professor Xinzhang Song (Zhejiang A&F University) et al. conducted a study that revealed the response of soil CO₂ emissions in bamboo forests of humid regions to straw mulching and its long-term effects. The research found that straw mulching not only significantly increased soil carbon emissions in the short term but also had enduring effects that persisted for at least three years after the removal of the mulching material. The study has been published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025607).

Diswandi Nurba from IPB University in Indonesia at al. systematically evaluated the performance of four aeration system designs through a combination of Computational Fluid Dynamics (CFD) simulations and AHP-TOPSIS multi-criteria decision analysis, providing a scientific answer to this problem. The study had been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024577).

An international team from countries including Iran, Iraq, Uzbekistan, and India has co-authored a review paper published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024564). The corresponding author is Dr. Mohammad MEHDIZADEH from University of Mohaghegh Ardabili. The article outlines the potential applications of machine learning technology in weed management and provides insights for addressing the aforementioned issues. In simple terms, machine learning acts like an “intelligent brain” for farmland——it can analyze vast amounts of agricultural data, automatically identify patterns, and make precise decisions, shifting weed control from a “broad net” approach to “precision strikes”.

Recently, Dr. Muhammad Waqar Akram and his team from the Department of Farm Machinery and Power at University of Agriculture Faisalabad in Pakistan developed a “Machine Vision-Based Automatic Fruit Grading System”, offering a new solution. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2023532).

Dr. Roaf Ahmad Parray from ICAR-indian agricultural research institute (ICAR-IARI) and his colleagues provide an answer in a study published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024572). In this research, an international team of scientists from India, Denmark, and the United States developed an innovative technology integrating spectral sensors, machine learning models, and an intelligent spraying system, successfully applying it to control black rot disease in cauliflower. This technology, comprising three core components—non-destructive detection, intelligent decision-making, and targeted pesticide application—significantly reduces pesticide use and offers new insights for green agriculture.

A new UC Riverside-led study reveals how common small particles produced by nature as well as human activities can transform upon entering plant cells and weaken plants’ ability to turn sunlight into food. The discovery offers a path to control this issue.