Acceptance of the University-Level Scientific Research Project: “Research on Developing an Artificial Intelligence-Based Solution to Support Early Warning of Strawberry Plant Disease Symptoms Based on Images”
On the morning of October 3, 2025, the acceptance council approved the project conducted by Dr. Tran Thuy Van and her research team, under the chairmanship of Dr. Nguyen Van Thien (born 1970) – Vice Rector of Hanoi University of Industry, who served as the Council Chairman.
The research team presented the project results to the Council.
Strawberry is a popular fruit due to its sweetness and rich nutritional content. It is widely cultivated in China, the United States, Poland, and Russia. In Vietnam, strawberries are mainly grown in Da Lat and several northern provinces. However, strawberries are also susceptible to diseases such as tip rot, fruit rot, and leaf scorch. These pathogens not only reduce photosynthetic efficiency but also negatively affect fruit quality, growth, and production.
Currently, disease detection in strawberries largely relies on manual inspection, which requires significant effort and time. The shrinking agricultural workforce further exacerbates this challenge, making it increasingly difficult to accurately predict disease severity on a large scale. Based on this practical need, Dr. Tran Thuy Van and her research team have implemented a highly accurate automated technique to enable early detection of diseases in strawberry plants.
The main results of the research team were published in 2024 in the international journal International Journal of Advanced Computer Science and Applications (ranked Q3). Additionally, the team developed an Android-based software module to recognize, process data, and provide early warnings. In the future, the research team plans to expand the dataset, investigate more advanced AI solutions, and integrate offline processing features to enhance diagnostic efficiency.
Regarding the research results, the Council noted that the summary report meets the professional requirements of a university-level scientific research project. Specifically, the report effectively presented both the scientific and practical foundations for applying the YOLOv8 network to detect and classify strawberry plant pathogens. The YOLOv8-based solution was experimentally implemented, validated, and analyzed, with commentary provided in a relatively comprehensive manner. To improve the report further, the Council suggested addressing layout errors, ensuring consistent use of English terminology, and preparing questions for professional discussions.
Dr. Tran Thuy Van – Project Manager, provided explanations before the Council.
Dr. Tran Thuy Van and her research team acknowledged the Council’s feedback and provided explanations for the issues raised during the acceptance session. The team affirmed that they would revise certain technical aspects to improve the overall quality of the report.
Dr. Nguyen Van Thien (1970) – Council Chairman, provided an overall evaluation of the acceptance session.
Dr. Nguyen Van Thien (1970), Council Chairman, evaluated the project as having significant practical value in agriculture and acknowledged the research team’s efforts in applying the YOLOv8 deep learning network for early detection and classification of strawberry diseases. The Chairman then requested the research team to incorporate the Council members’ feedback, revise the final report accordingly, and consider directions for the team’s future research development.
The acceptance council and the research team took a commemorative photo.
Based on the results of the session, the Council agreed to accept the project and rated it as Good.
