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. 

Since the mid-1800s, human activities have rapidly facilitated the spread of rice yellow mottle virus (RYMV), a pathogen that infects rice, far and wide across Africa, according to a new study led by Eugénie Hébrard, at the Institut de Recherche pour le Développement (IRD, France), published June 17, 2025 in the open-access journal PLOS Pathogens.

The global decline of wild bee populations is alarming. Landscapes characterised by intensive agriculture offer hardly any suitable habitats. Isolated local efforts are often not enough to counteract this loss. Now, researchers from the Universities of Göttingen and Halle show that combining certain agricultural and environmental measures at the landscape level can offer more protection for wild bees. Their findings show that organic farming combined with multi-year natural habitats – such as meadows planted with long-lasting, perennial plants – is particularly effective. These two types of habitats together support significantly more wild bees than either does on its own. The results were published in the Journal of Applied Ecology.