## 2 State of the Art
### Prediction of Product Behaviour during Transport and Handling  
Numerous studies have explored the challenges of freight damage during transit, focusing on how external factors during transportation and product handling could affect the integrity of goods. In [PERSON] et al. [19], the authors measured the vibration levels experienced by commercial truck shipments in Thailand and assessed their impact on tightly packed tangerines. The experiment evaluated how the combination of different sets of vehicles, vehicle speeds, and road types influenced the vibrations in the vehicles. The results indicated that higher truck speeds led to more significant damage to the transported fruit. Moreover, it was discovered that tangerines transported on laterite roads suffered the most damage and that fruit transported in 2 ton trucks incurred less damage compared to those in 6 ton trucks. An experiment with a similar goal, examining the impact of transport vibrations on apple bruising and evaluating the effectiveness of different packaging options for protecting the fruit, was presented in [PERSON] et al. [20]. Unlike the method used in [PERSON] et al. [19], this study employed an electro-dynamic shaker to simulate the vibrations. The research demonstrated that both the design of the packaging and the levels of vibration to which the vehicles were exposed had a significant influence on the extent of bruise damage suffered by the apples. Additionally, in [PERSON] et al. [21], the potential damage to frozen French-fried potatoes during transport and handling was investigated, examining shipments at both conventional and cryogenic temperatures. The study simulated in-transit vibrations and the effects of dropping or mishandling the product. The findings revealed that dropping the transported product was the primary cause of breakage. Studies exploring the interaction between road profiles, vehicle size, speed, suspension characteristics, and the dynamic behaviour of the load were presented in [PERSON] and [PERSON] [22] and [PERSON] and [PERSON] [23], where similar methodologies were employed. These studies found that, for loads of fruits and vegetables, there is a significant interaction between the energy absorbed by the cargo and the movement of the vehicle body. A few studies have adopted methodologies that integrate deep learning models to classify damage levels inflicted on products. The study in [PERSON] et al. [24] investigated the effects of rough handling activities, such as dropping, kicking, and throwing, on the internal acceleration of cargo. Based on the collected acceleration data, the authors developed a Convolutional Neural Network (CNN) to characterize the damage imposed on products. A similar methodology was proposed in [PERSON] and [PERSON] [25] focused on classifying damage levels in electronic assemblies subjected to high-acceleration mechanical shocks. The test product underwent six impacts using a drop tower; then, a Convolutional Variational Auto-Encoder (CVAE) model was developed, which enabled the detection of three distinct damage levels on the test product. The studies presented in [PERSON] et al. [26] and [PERSON] et al. [27] followed a unique approach by utilizing real-time sensor measurements to gather critical data on the condition of freight throughout its transportation journey. The study proposed in [PERSON] et al. [26] explored the use of temperature sensors in the transportation of perishable goods by developing Neural Network models. These models were designed to predict in-container temperatures based on measurements taken at the periphery of the containers, thereby providing a potential variable of interest in addressing cargo loss issues. While the work in [PERSON] et al. [27] also utilized real-time sensors, the suggested implementation pursued a different objective from that of the work in [PERSON] et al. [26]. In this study, acceleration sensors were embedded within the freight to facilitate data collection.