Abstract
The microclimate surrounding a plant has major effect on its health and photosynthesis process, where certain plants struggle in suboptimal environmental conditions and unbalanced levels of humidity and temperature. The ability to remotely track and correlate the effect of local environmental conditions on the healthy growth of plants can have great impact for increasing survival rate of plants and augmenting agriculture output. This necessitates the widespread distribution of lightweight sensory devices on the surface of each plant. Using flexible and biocompatible materials coupled with a smart compact design for a low power and lightweight system, we develop widely deployed, autonomous, and compliant wearables for plants. The demonstrated wearables integrate temperature, humidity and strain sensors, and can be intimately deployed on the soft surface of any plant to remotely and continuously evaluate optimal growth settings. This is enabled through simultaneous detection of environmental conditions while quantitatively tracking the growth rate (viz. elongation). Finally, we establish a nature-inspired origami-assembled 3D-printed "PlantCopter", used as a launching platform for our plant wearable to enable widespread microclimate monitoring in large fields. Flexible electronics: plants can now wear sensorsThe application of flexible electronics has been shifted from epidermal sensors to the surface of individual plant for microclimate and growth condition monitoring. Prof M. M. Hussain and colleagues from King Abdullah University of Science and Technology, Saudi Arabia develop wearable electronic system for plants with localized monitoring function. They borrow the wisdom from human wearable electronics to adopt and re-design the flexible, lightweight and biocompatible materials and electronic components to match the field requirements of plants-wearable electronics. They show integrated temperature, humidity and strain sensing and real-time plant growth monitoring and diagnosing. The approach reported in this work is potentially scalable to be applied to large fields and opens up the possibilities to customized investigation and optimization in plant sciences.