The objective of this study was to collect and interpret three-axis acceleration, temperature, and relative humidity data from six locations within commercial transport trailers shipping market-weight pigs. Transport was observed in Kansas (n = 15) and North Carolina (n = 20). Prior to loading, three-axis accelerometers were affixed to six locations on the trailers: top fore (TF), top center (TC), top aft (TA), bottom fore (BF), bottom center (BC), and bottom aft (BA) compartments. Data were post-processed to calculate root-mean-square (RMS) accelerations and vibration dose values (VDV) in the vertical direction and the horizontal plane. These values were compared with exposure action values (EAV) and exposure limit values (ELV), vibration levels deemed uncomfortable and potentially dangerous to humans. Additionally, RMS and VDV were compared among the trailer compartments. The vertical RMS accelerations for all compartments exceeded the EAV for loads measured in Kansas, and for the majority of the compartments measured in North Carolina. Many compartments, specifically the BA compartment from all trips, exceeded the vertical ELV. Regardless of where the data were collected, fewer compartments exceeded the EAV in the horizontal orientation. Only BA compartments exceeded the ELV in the horizontal orientation. There were Area × Level interactions for vertical and horizontal RMS and VDV (P < 0.01). The BF compartment had a greater vertical RMS value than the TF, TC, and BC (P < 0.02) compartments, but did not differ (P = 0.06) from the TA compartment. The vertical RMS of the TA compartment did not differ from the TF, TC, and BC compartments (P > 0.13). The BF compartment had a greater (P = 0.02) vertical VDV value than the TC location, but did not differ from the other locations (P > 0.16). All other locations did not differ in vertical VDV (P > 0.12). The BF compartment had greater horizontal RMS than the TC and TA compartments (P < 0.01), but did not differ from TF and BC compartments (P > 0.12). All other compartments did not differ in horizontal RMS (P > 0.34). All compartments, aside from the BA compartment, did not differ in horizontal VDV (P > 0.19). Vibration analyses indicated the BA compartment had the greatest vertical and horizontal vibrations and a large percentage of the compartments exceed the EAV and ELV, which indicated pigs may have experienced uncomfortable trips that could cause discomfort or fatigue.

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices.

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data were collected from a cultivator shank using the AVDAQ system. The relationship between shank vibrations and soil compaction, as measured by a cone penetrometer, was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. These findings demonstrate the feasibility of using vibrations generated during tillage to estimate soil compaction under real-time field conditions. With further validation, this approach could be integrated into tractors to enable in-situ soil sensing, reduce tillage intensity, and support more sustainable and energy-efficient cultivation practices.

Vibration measured at the cultivator shank is a rich but underutilized source of information about soil–tool interaction, which we propose can be read as a language of the implement’s engagement with the soil. As a first step of understanding this language, this study focuses on the vibration signature of the turning phase of the tillage operation. During a turn, the shank is lifted out of the soil, the turn is completed, and then reinserted into the soil. Using a convolutional neural network autoencoder, trained with a custom dual-domain loss function on primary tillage signals collected in 2022 on silt loam, a shared pattern was extracted with a mean Pearson correlation of 0.965. This pattern was then validated against independent 2023 primary tillage data, yielding strong agreement primarily in the frequency-energy distribution (FFT r = 0.93, PSD r = 0.99), while time-domain and time–frequency agreement were more moderate. When applied to a 2024 dataset from secondary tillage on Chase silty clay loam, the broad energy envelope was retained, but the primary-tillage time–frequency distribution was not reproduced. The proposed framework provides a practical foundation for future vibration-based interpretation of tillage operations without requiring traditional terramechanics assumptions.

The objective of this study was to measure and examine 3-axis acceleration data from 6 locations within commercial transport trailers shipping market pigs. Over winter months (December through February) of 2018 to 2019 and 2019 to 2020, 16 pot-belly and 14 straight-deck trailer loads of market pigs were measured from 2 producers located in Kansas and North Carolina, respectively. Six accelerometers were placed in protective cases and affixed to the underside of the floor in the approximate center of the front compartment, middle of the trailer, and back compartment of the top and bottom decks. Data were post-processed to calculate power spectral density (PSD) functions and corresponding root mean square (RMS) accelerations. The PSDs lend insight into the vibrational frequency content of the trailers, while the RMS values indicate the severity of the vibration over the duration of each trip. With the exception of the lower aft portion of the trailer where levels are significantly higher, RMS values were consistent across trips and largely similar between sensor location and axis. Accelerations ranged between 0.06 and 0.18 g and varied in time, indicating data were non-stationarity. The PSD results reveal a largely broadband frequency response of the loaded trailers between 0 and 50 Hz, especially for sensors on the lower deck. Preliminary analysis of the data indicates the severity of vibrations experienced by pigs during transport would be considered uncomfortable by humans.