The current work reveals the influence of loading parameters on the crack growth behavior of a Zr/Ti/steel composite plate with a crack normal to the interface by using an experiment and the finite element method. The Chaboche model was first used to study cyclic plastic evolution in composite materials. The results reveal that an increase in Fmax, Fm, and Fa can promote da/dN; meanwhile, an increase in R will reduce da/dN. The plastic strain accumulation results indicate that Fm mainly contributes to the tensile strain and compressive stress after the first cycle. Additionally, Fa increases the stress range and compression stress and greatly improves the plastic strain accumulation degree in subsequent loading cycles. The Fmax can significantly increase the stress amplitude and plastic strain accumulation level. When R increases, the plastic strain accumulation increases a little, but the stress amplitude and compression stress decrease greatly. Furthermore, it is also found that the elastic–plastic mismatch also affects the plastic evolution, that is, strengthening or weakening the effect of the loading parameters.

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Current work reveals the influence of loading parameters on the crack growth behavior of Zr/Ti/steel composite plate with a crack perpendicular to the interface by using experiment and finite element method. Finite element results reveal that loading parameters significantly affect hysteresis loop of Zr/Ti/steel composite plate, stress amplitude and the plastic strain accumulation degree of subsequent cyclic loading under different loading parameters. In addition, equivalent plastic strain at crack tip, its change rate and distribution are also explored in detail. Furthermore, it is also found that the elastic-plastic mismatch will also affect the plastic evolution, that is, strengthen or weaken the effect of loading parameters.

Background Currently, the variable‐rate application (VA) of agrochemicals on fruit trees is based on canopy volume and biomass. The canopy volume has a significant relationship with disease resistance and degree of disease incidence. Therefore, this study proposes a VA method that uses deep convolutional neural networks for real‐time recognition of disease spots on pear trees. Furthermore, it specifies the limitations and application scenarios of the disease spot recognition. Field performance tests were conducted to verify the performance of the proposed VA system. Results The results showed a mean average precision, precision, and recall of 87.42%, 83.76%, and 87.23%, respectively. The spot recognition rate was 81.3% when the canopy sampling distance, spot diameter, and canopy porosity were 1.2 m, 4–8 mm, and 55.76%, respectively. The results indicate that the proposed VA system saved 51.9% spray volume compared to conventional methods while ensuring quality. Conclusion Compared to the traditional constant rate model, the proposed VA technology based on real‐time disease spot identification can reduce spraying in nondiseased areas, thereby abandoning the previous saturation application practice and significantly reducing pesticide use. © 2022 Society of Chemical Industry.