Toward robust in-field fruit quality evaluation: A critical review of emerging nondestructive technologies and...

Fruit is a vital component of the human diet, and its quality attributes are of major concern to consumers. Fruit quality is primarily enhanced during the on-tree growth phase. In-field fruit quality detection provides critical data for precision orchard management, thereby enhancing fruit quality at the source of the supply chain. Driven by growing demand, research on in-field fruit quality detection is rapidly expanding. Therefore, a comprehensive review is essential to track the state-of-the-art inspection technologies and devices tailored to orchard environments. This review systematically summarizes recent advances in promising in-field fruit quality detection technologies, which primarily rely on instrumentation based on mechanical, acoustic vibrational, optical, and electrochemical principles. It evaluates the application scenarios and performance of current portable devices, wearable sensors, noncontact devices, and flexible robotic manipulators integrated with quality sensing capabilities. Furthermore, potential pathways are outlined to facilitate the transition of fruit quality detection technologies from postharvest applications to field-based implementation. Different detection technologies and devices are suited to specific application scenarios. Portable devices leverage miniaturization and cost-effectiveness for routine spot checks. Wearable sensors enable continuous monitoring of long-term fruit quality changes. Noncontact devices achieve orchard-scale assessments through broad spatial coverage. Flexible robotic manipulators with quality sensing capabilities allow integrated harvesting and grading operations. However, challenges remain in terms of robustness, efficiency, and cost-effectiveness under real orchard conditions. Future developments will focus on achieving intelligent and automatic in-field fruit quality detection through innovations in micro-nano fabrication, structural design, advanced algorithms, and the integration of multiple sensors.