On-Demand Design of Terahertz Metasurface Sensors for Detecting Plant Endogenous and Exogenous Molecules

Hongyan Gao (), Yuanye Liu, Gen Li, Haodong Liu, Yuxi Shang and Zheng Ma
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Hongyan Gao: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yuanye Liu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Gen Li: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Haodong Liu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yuxi Shang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zheng Ma: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 14, 1-20

Abstract: This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to map the complex relationship between them. Two single-resonant-peak metasurface sensors were developed to detect abscisic acid and gibberellic acid. The abscisic acid metasurface sensor achieved an average MSE of 5.66 × 10 −6 and R ER of 0.167%, while the gibberellic acid metasurface sensor had an average MSE of 8 × 10 −7 and R ER of 0.086%. Their resonant peaks highly matched the substance fingerprint peaks, enabling specific detection. Metasurface sensors’ sensitivities were effectively controlled using correlation analysis and neural networks, achieving remarkable levels of 156.7 and 150.1 GHz/RIU, allowing trace detection. Three dual-resonant-peak metasurface sensors were designed to improve the detection specificity for chlorophyll and folpet and to detect chlorophyll and folpet simultaneously. These metasurface sensors exhibited average MSEs of 1.4 × 10 −5 , 1.6 × 10 −6 , 1.35 × 10 −5 and R ER s of 0.27%, 0.088%, 0.20%. The model also worked for four other plant-related molecules, proving its strong generalization ability. Overall, for different application scenarios of exogenous and endogenous molecules in plants, the on-demand design methodology offers a whole new set of ideas for quickly designing and widely applying metasurface sensors with suitable performance indicators.

Keywords: plant molecule; terahertz; metasurface; neural network; on-demand design (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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