Vietnam Harnesses AI to Revolutionize Agricultural Product Quality Assessment

A research team from the Center for High Technology Innovation (Vietnam Academy of Science and Technology) has successfully developed an AI-powered quality assessment system for agricultural products. This technology specifically aims to enhance quality control and reduce post-harvest losses for key exports like mangoes and dragon fruits. Vietnam faces significant challenges in ensuring the quality and safety of its agricultural products, particularly its main fruit exports. Post-harvest losses for mangoes and dragon fruits currently range from 20% to 30%, causing substantial damage to farmers and impacting international competitiveness. Furthermore, the demand for controlling chemical residues and heavy metals in exported produce is increasingly stringent. Traditional quality assessment methods, relying heavily on practical experience combined with physicochemical and microbial analyses, are often time-consuming, costly, and can damage product samples. In response to these issues, Dr. Bui Quang Minh and his colleagues have led the initiative "Research on the Application of Artificial Intelligence in Assessing the Quality of Agricultural Products." This project involved collecting a database of approximately 10,000 images detailing the shape, size, and nutritional content of mangoes and dragon fruits. They analyzed the quality of these fruits using chemical methods integrated with image analysis to find correlations between quality and visual characteristics. The team also built a database of the physicochemical properties of mangoes and dragon fruits and developed an AI software system for their quality assessment. Key growing regions such as Dong Thap province, Can Tho city, Long An province, and Dong Nai province were selected for this research. According to Dr. Bui Quang Minh, the project lead, applying AI in agricultural product assessment is no longer confined to laboratory settings. The technology is built upon deep learning models combined with image processing techniques to detect, classify, and evaluate the quality of agricultural products. Successful implementation of this AI technology will help minimize losses and contribute to enhancing the productivity, quality, and international reputation of Vietnamese agricultural products. Moreover, the AI technology integrates hyperspectral imaging (HSI) techniques with complex machine learning models. This allows for the analysis of the spectral composition of agricultural products, enabling accurate estimation of nutrient content, moisture, and internal quality without destructive sample analysis, thereby shortening laboratory analysis times. The conversion from RGB images to hyperspectral images via HSI is also more cost-effective and faster, providing real-time quality results in the field. To ensure product traceability and transparency of quality information from harvest to consumer, AI is integrated into the agricultural value chain through combination with blockchain technology. Additionally, AI applications allow for quality monitoring at each stage, improving the efficiency of warehousing, transportation, and distribution. Predictive algorithms based on environmental data, such as temperature, humidity, soil pH, and crop growth indices, also assist farmers in determining optimal harvest times, reducing post-harvest losses, and increasing profits. During the project, the research team successfully built a dataset comprising 14,411 images of mangoes and dragon fruits of varying quality. This serves as a crucial raw data source for training, testing, and operating the AI model for agricultural product quality assessment. Furthermore, the project developed the "Fruit Monitor/Fruit AI" software, capable of recognizing, assessing, and alerting on the quality of these agricultural products with over 90% accuracy. The software system also allows for the management of user data, farms, storage facilities, cameras, AI models, and training history. A process for applying the AI system in assessing the quality of these fruits has been established, involving steps such as image collection and preprocessing, data labeling, chemical component analysis of mangoes and dragon fruits at different quality levels, AI model construction, training, and deployment on web and mobile platforms. However, the application of AI for agricultural product quality assessment requires robust storage and cloud computing infrastructure to handle large volumes of data from sensors and images, along with stable internet bandwidth for transmission to analysis centers. The initial investment costs for specialized cameras, HSI sensors, and AI software remain high, posing a barrier for small-scale farmers. Training personnel to operate, maintain, and develop AI technology in rural areas is essential but currently limited. Government and international organizations need to ensure legal frameworks for data security, privacy, and unified quality standards. The project has published three SCIE international papers and two national papers, and has trained one Master's student. The Vietnam Academy of Science and Technology's acceptance council has recognized the "Research on the Application of Artificial Intelligence in Assessing the Quality of Agricultural Products" project as having significant scientific and practical value, contributing to improved agricultural product quality assessment and promoting AI adoption in agriculture. Source: Nhan Dan