A Methodology for Combine Performance Analyses in Wheat Harvests with GNSS Data

Abstract

Highlights Proposed a novel methodology for fully automated, low-cost, and high-resolution harvest performance analyses. Described methods for estimating key features, such as the center of the header, using noisy positioning data. Introduced metrics Swath Utilization and Spatial Field Capacity to evaluate temporal and spatial performances. Provided case studies of using these two new metrics to compare combine performances by machines and by years. Abstract. Combine harvesters’ performance during wheat harvests can be analyzed using various methods. These methods typically rely on traditional field-level metrics, such as those defined by ASABE, to address average performances in terms of field or machine. However, next-generation digital agriculture technologies have significantly increased the operation precision of agricultural activities. As a result, the evaluation of instantaneous performance becomes possible. This work introduces a novel methodology that enables fully automated, low-cost, and high-resolution (both in time and space) instantaneous combine performance analyses based on global navigation satellite system (GNSS) positioning records. The methodology incorporates a multi-step, easy-to-follow workflow with customizable modules for efficient and effective data processing. This way, the computation of traditional field capacity metrics can be fully automated even if multiple combines cooperate in harvesting the same field. Furthermore, two groups of novel metrics are proposed: Swath Utilization and Spatial Field Capacity. They enhance traditional metrics by analyzing machine performances both temporally and spatially on a finer scale. As a case study, we computed these metrics for seven fields in Colorado, USA, during wheat harvests across five different years. We compared the results with typical values from ASABE standards to validate the correctness of our data processing methodology. We also provided four analysis examples with a rich set of temporal and spatial visualizations to showcase how our metrics can accurately assess combine performances, quantitatively uncover harvest details, and effectively compare operations in different fields/years for better practice. These new analyses enabled by our methodology are required to harness the full potential of digital agriculture. Keywords: Combine harvester, Field capacity, Global navigation satellite system (GNSS), Kalman filter, Optimization, Positioning data, Wheat harvest performance.