Advancing post-harvest fruit handling through AI-based thermal imaging: applications, challenges, and future trends

Abstract

Abstract Recent advancements in imaging, electronics, and computer science have engendered significant progress in non-destructive testing and quality monitoring within the agro-food industry. This progress is particularly evident in integrating infrared thermal imaging (TI) and artificial intelligence (AI) techniques. As a non-contact method, AI-based TI holds promise in detecting various quality attributes and has found extensive applications in agriculture, food processing, and post-harvest fruit handling. This paper delves into recent applications of AI-based thermal imaging, specifically in post-harvest fruit handling. The introduction provides a comprehensive overview of the challenges faced in the post-harvest fruit handling industry while emphasizing the advantages of AI-driven thermal imaging technology. The detailed thermal imaging system encompasses both passive and active thermography techniques. This paper provides an in-depth exploration of artificial intelligence, focusing on machine learning and deep learning. It highlights the significance of convolutional neural networks (CNNs) and their architectural phases. Subsequently, critical applications of AI-based thermal imaging in post-harvest fruit quality assessment are discussed. These applications encompass bruise detection, maturity identification, condition monitoring, grading and sorting, pest and disease detection, and considerations for packaging and supply chain management. Furthermore, this paper addresses the challenges and limitations of AI-based thermal imaging in post-harvest fruit handling. In conclusion, this paper discusses future trends in AI-based thermal imaging, emphasizing the potential for increased automation and integration with emerging technologies in the post-harvest fruit handling sector. The insights provided contribute to the ongoing dialog surrounding optimizing quality assessment processes in the agro-food industry.

Keywords

• AI-based thermal imaging
• Post-harvest fruit handling
• Quality assessment
• Non-destructive testing
• Bruise detection
• Maturity identification