Design Optimization of a Root-Targeted Steam Injection Module for Sustainable Thermal Weed Management

Sustainable agricultural production requires environmentally friendly weed management solutions. Steam-based thermal weed control is a promising alternative to conventional herbicide-based weed management. However, optimizing steam delivery systems remains computationally expensive due to the repeated simulation-based design evaluations required. This paper presents a design optimization framework for a novel root-targeted steam injection module intended for integration into an autonomous agricultural platform for sustainable thermal weed management. The mechanical behavior of different nozzle geometries was evaluated using finite element analysis using the ABAQUS/CAE software, generating the simulation dataset used for optimization. To reduce the computational cost associated with repeated finite element simulations, a Kriging surrogate model was constructed from this dataset and coupled with an evolutionary optimization algorithm to identify the optimal nozzle geometry. The evaluated nozzle geometries exhibited maximum stresses ranging from 5.12 to 20.41 N/mm2. The stress value predicted by the Kriging model for the optimized configuration was validated through an additional finite element simulation, showing a deviation of only 6.6%. Furthermore, an artificial neural network model implemented in PyTorch 2.7.1 was trained on the simulation dataset and used as an independent validation tool to estimate the stress corresponding to the optimized nozzle geometry, with a prediction deviating by approximately 5.4% from the corresponding finite element result. The proposed approach significantly reduces computational cost while maintaining high accuracy in stress prediction, enabling the identification of a structurally reliable nozzle geometry for sustainable herbicide-free thermal weed management.