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Collision of falling in the mechanical harvesting process of sweet potato is one of the main causes of epidermal destruction and damage to sweet potato tubers. Therefore, a sweet potato mechanical characteristic test and a full-factor sweet potato drop test were designed. Based on the analysis of the fitting mathematical model, the impact of the drop height, collision material and sweet potato chunk size on the damage of the sweet potato were studied. The mathematical models were established by fitting analysis of the IBM SPSS Statistics 22 software between the drop height and the sweet potato chunk size with each test index (impact force, impact stress, broken skin area and damaged area). The critical epidermal destruction height and the critical damage height of a certain size of sweet potato when it collides with a collision material can be calculated by the mathematical model, and the critical epidermal destruction mass and critical damage mass of sweet potato when it falls from a certain height and collides with a collision material can also be calculated. Then a series of critical values (including critical epidermal destruction force value, critical epidermal destruction impact stress, critical damage force value, critical damage impact stress) of mechanical properties of sweet potato were obtained. The results show that the impact deformation of sweet potato includes both elastic and plastic ones, and has similar stress relaxation characteristics. The critical damage impact stress of sweet potato is that the average value of the impact stress on the contact surface is less than it's Firmness. The results provided a theoretical basis for understanding the collision damage mechanism of sweet potato and how to reduce the damage during harvest., Competing Interests: The authors have declared that no competing interests exist.

Mechanical parameters used in many design codes can be achieved by expensive and time-consuming experiments or by non-destructive approaches such as estimative modelling. This investigation proposed Extreme Gradient Boost (XGB) for estimating the slump (SL) and compressive strength (CS) of high-performance concrete (HPC). In addition, to bring the results of the models closer to the experimental data and increase the accuracy, algorithms were combined with the model, including Sunflower Optimizer (SFO) and Jellyfish Search Optimize (JSO). The relevant models have been examined in three frameworks: individual, hybrid, and ensemble-hybrid. For this purpose, several evaluators were provided to determine the errors, compare, and accuracy of the presented models. The XGFJ model has demonstrated exceptional performance, achieving remarkable results in terms of RMSE (Root Mean Square Error) and R2 (R-squared) values. Specifically, it has attained an exceptionally small RMSE value of 1.785 for CS and 5.183 for SL, indicating the model's high precision in predicting these parameters. Additionally, it has achieved the biggest R2 values of 0.9960 for CS and 0.9949 for SL. Additionally, it is worth noting that the XGSF model closely matches the performance of the ensemble form of XGFJ, as evident from its R2 values of 0.9956 for CS and 0.9934 for SL. Based on the study, it was observed that using machine learning to anticipate the mechanical characteristics of concrete is valuable and efficient and can be considered an alternative method instead of time-consuming laboratory methods. This research addresses challenges in predicting HPC properties fueled by the need to overcome drawbacks in traditional methods. Costly and time-intensive laboratory experiments prompted the exploration of alternatives, leading to the proposal of XGB combined with optimization algorithms (SFO and JSO). The study aims to enhance prediction accuracy while tackling broader concerns such as construction costs, material efficiency, and environmental impact. The resource-intensive nature of conventional methods, along with inaccuracies due to material variations, serves as a primary challenge. The proposed resolution advocates for a paradigm shift to machine learning, exemplified by the XGFJ model, showcasing exceptional precision and efficiency in predicting HPC properties. [ABSTRACT FROM AUTHOR]