Digitized Seedbed Soil Quality Assessment from Worn and Edge Hardened Cultivator Sweeps

Tillage tools for seedbed soil management are often subjected to low stress abrasion wear, which could negatively affect seedbed quality and crop productivity. Limited studies exist that quantify the effects of worn tillage tools on seedbed quality and crop yield. This research investigated the influence of tillage tool wear on seedbed preparation by evaluating the effect of cultivator sweep wear on soil tilth utilizing a light detection and ranging (LiDAR) sensor. The framework consists of a seedbed tillage field experiment using a Completely Randomized Design (CRD) experiment in six replicates of two-tillage treatments (new and worn cultivator sweeps). After seedbed tillage, loosely tilled soil aggregates were removed to expose the seedbed soil profile, and then seedbed roughness statistical measures were estimated from LiDAR-scanned seedbed soil surface. Three statistical analyses (Analysis of Variance (ANOVA), Kolmogorov-Smirnov (KS), and Earth Mover's Distance (EMD)) were compared to quantitatively evaluate the soil roughness estimated from the LiDAR seedbed surface data. Seedbed prepared by new and worn cultivator sweeps showed significant differences (p < 0.05) in soil roughness variables of standard deviation, coefficient of variation, and kurtosis. Data analysis from the ANOVA and KS methods revealed that LiDAR-extracted soil roughness patterns were statistically influenced by tillage treatment. EMD analysis detected noticeable disparities between the tillage treatments and new versus worn cultivator sweeps. This study concludes that tillage tool wear substantively affects seedbed quality, as evidenced by LiDAR soil profile estimated attributes of soil roughness and three statistical methods (ANOVA, KS, and EMD). Our study supports the adoption of LiDAR technology for seedbed management, highlighting its applicability to evaluate seedbed quality that accounts for the wear life cycle of cultivator sweeps.