Data-driven approaches to improve seed depth consistency in precision planting system

Abstract

Planting is one of the most crucial operations in crop production, as it determines how well a crop establishes and performs throughout the season. The goal of planting is to place each seed at an appropriate spacing and depth in the soil in a timely manner. While spacing ensures desired plant stand, accurate seeding depth ensures proper seed-to-soil contact, placement in adequate moisture zone, and favorable temperature conditions for uniform germination for all seeds. Consistent seeding depth plays a critical role in early-stage crop performance such as emergence rate and plant density, key factors influencing final yield. Despite major advances in precision planting technology, good is still a major challenge, especially when moisture plays a critical role in initial plant establishment. However, there is a need for technology which can not only place the seed in the right moisture zone by real-time changing seeding depth but can also provide uniform emergence to achieve optimal yields. This research was designed to address key questions in precision planting, 1) How margin selection and operating speed impact GWL and seed depth uniformity 2) Can a variable depth seeding system provide accurate seeding depth at varying target depths under variable field conditions and, 3) Can a variable-depth seeding system promote uniform emergence for achieving optimal yields. To answer these, two complementary studies were conducted focusing on planter mechanics and agronomic outcomes. The first study, Optimization of Gauge Wheel Load (GWL) Margin to Improve Planter Performance, evaluated the effects of four downforce margins (445 N, 778 N, 1112 N, 1446 N) and three planting speeds (8, 12, 16 km h⁻¹) on seeding depth consistency. This study was conducted to see if the planter can manage uniform downforce management, one key factor when implementing seeding. Field data on seed depth, GWL, speed, and row-unit acceleration were analyzed using linear regression and visualization methods. Results showed that the control system implemented target GWL, and within GWL margins around 1112 N produced the most consistent seed depth (30% lower variability). Higher and more stable GWL margins improved soil contact, reduced compaction, and enhanced planter stability across varying field conditions. The second study, Quantifying Emergence Uniformity Using an Electronic Depth (E-Depth) Control System, examined how planting depth affected emergence timing and yield across different fields. Three target depths (1.5, 2.0, and 2.5 in) were implemented using a precision planter equipped with real-time electronic depth control. Data on emergence, actual depth, and yield were analyzed using logistic regression and ANOVA. The 2.0-inch depth consistently resulted in faster, more uniform emergence and more stable yields under diverse field environments, while the deeper 2.5-inch depth resulted in delayed emergence that required on average approximately 5 additional days to reach 95% emergence. These studies provide a comprehensive understanding of how optimizing both mechanical (GWL margin) and agronomic parameters can achieve precise and consistent seeding depth. The findings deliver data-driven insights to improve planter performance, enhance stand establishment, and support smarter, digital-agriculture decisions for producers.