Advancing tea detection with artificial intelligence: Strategies, progress, and future prospects

Background: Tea is a vital economic crop in developing countries, crucial for rural development, poverty reduction, and food security. Tea consumption offers health benefits due to its anti-inflammatory and antioxidant properties. Achieving sustainable development of the tea value chain from field to cup is a shared goal of all humanity. Artificial intelligence algorithms enhance the efficiency and accuracy of tea quality testing when integrated with emerging technologies, thereby promoting the healthy and sustainable development of the tea industry. Scope and approach: This paper reviews the common machine learning and deep learning algorithms in artificial intelligence, outlining their advantages and limitations. It focuses on applying sensor technology and spectral technology, assisted by artificial intelligence algorithms, efficiently detecting tea quality. Finally, the paper summarizes the advancements in AI algorithms for tea safety detection and classification. It discusses the challenges and future prospects of sensor and spectral technologies and artificial intelligence in tea quality testing. Key findings and conclusions: Artificial intelligence algorithms' efficient pattern recognition and rapid adaptation to new data drive innovation in data-driven decision-making and technological development. Although significant achievements in tea and food quality and safety testing have been made using sensor and spectral technologies assisted by artificial intelligence, considerable potential for further development remains. Integrating artificial intelligence with various emerging technologies enhances comprehensive and in-depth support for tea quality and safety testing, thus safeguarding public health and safety.