Evan Ridder

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.