Abstract With the fast‐paced development of biomedical electronics, monitoring physiological processes have become ubiquitous throughout the field of implantable devices. Nevertheless, inherent challenges remain extant when long‐term applications are concerned. For the stable and reliable function of these devices, hermetic and biocompatible encapsulation is of paramount importance; however, extrinsic defects and intrinsic swelling properties of the encapsulating layer present the key limitation to ideal barrier performance. Thus, the ability to monitor biofluid penetration and predict the device's functional lifespan is necessary for safe and stable operation within the body. This paper presents the functional encapsulation structure that quantitatively measures the diffusion of fluids into the encapsulation layer. The hydrolysis of Magnesium (Mg) electrodes underneath the encapsulating material shows the capability to wirelessly monitor the water penetration rate and the presence of defects, such as pinholes and cracks, in the encapsulating material. The experiments conducted throughout this paper analyze the Mg thickness and geometry of the antenna to optimize the device's susceptivity to water penetration when submerged in aqueous environments. The facile fabrication process and the compatibility with prevailing implantable electronics further substantiate the device's usability in diverse applications where chronic implants are necessary for monitoring disease or administering required treatments.

• Novel Transformer-based model enables direct control of automated greenhouse. • Trans-Farmer learns hidden relations between environmental and control variables. • Trans-Farmer outperforms all baselines on 3 evaluation metrics for control tasks. • Multi-head attention mechanism provides interpretable control decisions. Climate change poses a significant threat to agricultural sustainability and food security. Automated greenhouse systems, which provide stable and controlled environments for crop cultivation, have emerged as a promising solution. However, traditional rule-based greenhouse control algorithms struggle to determine optimal control variables due to the complex relationships between environmental variables. In response, we propose a Transformer-based model, Trans-Farmer, which predicts the control variables by considering the complex interactions among environmental variables. Trans-Farmer leverages the attention mechanism to learn the intricate relationships among the environmental variables. The encoder-decoder structure enables the translation of the environmental variables into the corresponding control variables, analogous to language translation. Experimental results demonstrate that Trans-Farmer outperforms baseline models across all the evaluation metrics, achieving superior accuracy and predictive performance. The attention maps of the encoder visualize how Trans-Farmer comprehends the complex interactions among the environmental variables. Additionally, the compact size of Trans-Farmer is suitable for application in general greenhouses with constrained microcontroller units. This approach contributes to the development of automated greenhouse management systems and emphasizes the potential of artificial intelligence applications in agriculture.