Warm congratulations to Changchun Vocational Technical University on winning the Gold Award!

At the recently concluded 2025 World Vocational Skills Competition—Modern Agriculture Track, the team from the College of Modern Agriculture at Changchun Vocational Technical University clinched the gold medal with its “Green Tentacles” project. This prestigious honor not only underscores Changchun Vocational Technical University’s profound expertise and robust capabilities in agricultural science and technology but also paves the way for entirely new technological approaches to smart agriculture, vividly embodying the innovative principle of “technology for the people.” As a supporting partner of this event, QiuShi Spectrum is deeply inspired and proud, and hereby extends its warmest congratulations to Changchun Vocational Technical University!

Figure 1: The team wins the Gold Award in the Modern Agriculture Track Competition

The success of the “Green Antennae” project hinges on the deep integration and innovative application of cutting-edge technologies. By innovatively combining multispectral imaging, artificial intelligence, and the Internet of Things, the project has established a comprehensive health-monitoring system that spans the entire growth cycle of tomatoes. Among these innovations, multispectral early-diagnosis technology stands out as the core breakthrough, serving as a “technological vanguard” for safeguarding tomato cultivation.

Figure 2: CM020D multispectral camera used in the project

Multispectral cameras can accurately capture spectral data of tomato leaves across multiple wavelength bands. When tomato plants are infected by pathogens, subtle changes occur in leaf cell structure, chlorophyll content, and water status—“health signals” that are difficult to detect with the naked eye but are clearly reflected in specific alterations in spectral reflectance. Through sophisticated analysis of the spectral data, the project team can issue early warnings before visible symptoms of disease or pest infestation appear, thereby gaining valuable time for timely intervention and control in agricultural production.

Figure 3: Pseudocolor image after NDVI calculation

At the data-application level, the project team has developed diagnostic models centered on deep-learning techniques. By training on vast amounts of image data, these models achieve 96.7% accuracy in identifying common tomato diseases such as early blight, late blight, and gray mold. In addition, the team has integrated the YOLO algorithm to create a pest-and-disease identification system, which is deployed on edge-computing devices to enable real-time detection and visualized result presentation, thereby significantly lowering the barrier to technology adoption. Currently, this system is deeply integrated with intelligent agricultural-robotic equipment, forming an end-to-end tomato-health-monitoring framework that encompasses perception, analysis, decision-making, and execution. Behind this achievement lies both the concentrated embodiment of the research expertise of the Changzhi University team and strong validation of the practical value of QiuShi’s hyperspectral and multispectral technologies in smart-agriculture applications.

Congratulations to the team from the College of Modern Agriculture at Changchun Vocational Technical University on their outstanding achievements! We look forward to collaborating with Changchun Vocational Technical University in the future to explore new possibilities in agricultural science and technology and to write a new chapter in smart agriculture.