This paper mainly proposes two kinds of artificial neural network (ANN) models for predicting the plastic anisotropy properties of sheet metal using spherical indentation test, which minimizes measurement time, costs, and simplifies the process of obtaining the anisotropy properties than the conventional tensile test. The proposed ANN models for predicting anisotropic properties can replace the traditional complex dimensionless analysis. Moreover, this paper is not limited to the prediction of yield strength anisotropy but also further accurately predicts the Lankford coefficient in different orientations. We newly construct an FE spherical indentation model, which is suitable for sheet metal in consideration of actual compliance. To obtain a large dataset for training the ANN, the constructed FE model is utilized to simulate pure and alloyed engineering metals with one thousand elastoplastic parameter conditions. We suggest the specific variables of the residual indentation mark as input parameters, also with the indentation load–depth curve. The profile of the residual indentation, including the height and length in different orientations, are used to analyze the anisotropic properties of the material. Experimental validations have been conducted with three different sheet alloys, TRIP1180 steel, zinc alloy, and aluminum alloy 6063-T6, comparing the proposed ANN model and the uniaxial tensile test. In addition, machine vision was used to efficiently analyze the residual indentation marks and automatically measure the indentation profiles in different orientations. The proposed ANN model exhibits remarkable performance in the prediction of the flow curves and Lankford coefficient of different orientations.

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Uniconazole (UCZ), as a plant growth regulator, has been extensively applied in sweetpotato (Ipomoea batatas (L.) Lam) to increase tuberous root yield and quality. It is usually used in the production of sweetpotato by foliar spray. The post-harvest storage stage is crucial for forming the quality of the sweetpotato’s tuberous root. Few studies have focused on the foliar spraying UCZ-affected storage quality of sweetpotato during pro-harvest storage. To examine the effects of foliar application of UCZ on the storage quality of tuberous root, this study mainly analyzed the influence of storage quality, with (K2 and K4) and without (K1 and K3) 100 mg·L−1 foliar spraying of UCZ, at a storage period of normal fertilizing treatments (K1 and K2) and rich fertilizing treatments (K3 and K4), on the storage quality of three representative sweetpotato varieties (Z13, Z33 and J26). Compared to the no-use UCZ treatments, the decay rate of K2 was the lowest for any storage time. The decay rate of all the varieties was 0.0% before 45 DAS. Only the decay rate of Z33 increased to 4.4% at 60 DAS (p < 0.05). The dry matter rate of K2 and K4 was still higher than that of K1 during 15–60 DAS in Z13 and J26 (p < 0.05). UCZ foliar spraying was higher than without treatment at 30–60 DAS. In Z33, the springiness of UCZ spraying was higher than no spraying treatments at 45–60 DAS. These results indicate that foliar spraying of UCZ had no effect on the storage quality of tuberous root decreasing sharply, and it sometimes kept the quality stable.