Min Xue, Xin Li, Jinbang Wang, Bingbing Gao
Plant microneedles serve as a minimally invasive biointerface capable of fluid sampling, in situ sensing, and targeted delivery, offering a transformative strategy to overcome limitations in conventional physiological monitoring and agrochemical application. Artificial intelligence (AI)-assisted and data-driven modeling is emerging as a promising approach to accelerate material selection, structural refinement, and process optimization for plant MN technologies. The integration of predictive analytics with interface engineering offers a new paradigm for rational design and closed-loop plant monitoring, advancing MN platforms toward intelligent and adaptive precision agriculture.
The lack of spatiotemporal precision in conventional physiological monitoring and agrochemical application hinders real-time plant intervention. Plant microneedles serve as a minimally invasive biointerface capable of fluid sampling, in situ sensing, and targeted delivery, offering a transformative strategy to overcome these persistent limitations. Microneedle (MN) technology has emerged as a promising minimally invasive interface platform that enables interstitial fluid sampling, in situ sensing, and targeted substance delivery within plant tissues. Despite its growing potential in agriculture and plant science, plant MN systems still encounter challenges in interfacial stability, multifunctional integration, rational design optimization, and scalable manufacturing. Herein, this review provides an interface-engineering perspective on plant MN technologies, encompassing material systems, structural configurations, fabrication strategies, sensing mechanisms, and agricultural applications. Beyond summarizing recent advances, we further highlight the emerging role of artificial intelligence (AI)-assisted and data-driven modeling in accelerating material selection, structural refinement, and process optimization. The integration of predictive analytics with interface engineering offers a new paradigm for rational design and closed-loop plant monitoring, advancing MN platforms toward intelligent and adaptive precision agriculture.