A research team has developed an advanced computer vision model that can detect the precise growth points of weeds in cornfields with unprecedented accuracy.

A research team has developed advanced methodologies for predicting the aboveground biomass (AGB) of corn by integrating unmanned aerial vehicles (UAVs), multi-sensor data, and machine learning models.

A research team has now introduced the concept of the photochemical compensation point (PCCP), a novel measure that identifies the light level at which energy used for photosynthesis equals that dissipated non-photochemically.

A research team sheds light on how canopy structure and photosynthetic traits in coniferous forests respond to thinning, drought, and rising temperatures.

A new study introduces a universal two-stage method that successfully segments plant stems and leaves across both monocotyledonous and dicotyledonous crops.

A research team has demonstrated that greenhouse tomato productivity can be significantly improved by targeting leaf-level efficiency and plant layout strategies.

Recently, the team pf researchers led by Professor Wen Xu from China Agricultural University took the Haixi area of the Erhai Lake Basin as the research object. By integrating farmer surveys, literature materials and statistical data, they systematically quantified the emissions of four pollutants, namely ammonia nitrogen (NH₃-N), total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD), from agricultural production and rural domestic sewage in this area in 2022. The related paper has benn published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025622).