FlowDelayNet: Self-Supervised Siamese Learning for Automated Flow-Delay Estimation in Yield Monitor Data
ORCID
Drewry: https://orcid.org/0000-0003-3221-4364
MSU Affiliation
College of Agriculture and Life Sciences; James Worth Bagley College of Engineering; Department of Agricultural and Biological Engineering
Creation Date
2026-10-02
Abstract
Yield monitor data are widely used for management zone delineation, prescription map development, and on-farm decision making; however, delay in the data caused by sensor response latency can misassign yield measurements to incorrect locations. Existing correction methods often rely on manual tuning, or handcrafted similarity measures that are sensitive to noise, missing data, and irregular harvesting geometry. This study proposes FlowDelayNet, a self-supervised deep learning framework for estimating field-level flow delay by learning delay-aware spatial representations. FlowDelayNet employs a Siamese convolutional encoder trained to discriminate whether two yield patches have similar or dissimilar delays. A dataset of 632 harvested fields was split at the field level into training (70%), validation (15%), and testing (15%). Yield surfaces were rasterized over candidate delays from −15 to +15 logging intervals, and overlapping patches (24 × 24, stride 12) were extracted. A Gaussian-based spatial smoothness score computed using correlation analysis provided the self-supervised supervisory signal. Model optimization combined contrastive loss on Siamese embeddings with regression loss against the normalized smoothness signal. Compared to hard-argmax selection, a temperature-scaled soft-argmax formulation improves delay estimation by computing the expected delay. Across 95 fields, the mean absolute error was 1.24 delay units, with 64 fields predicted within ±1 delay relative to the Gaussian spatial smoothness reference. Embedding analysis showed a mean anchor–positive distance of 0.25 compared to 0.72 for anchor–negative pairs, with 94.8% of sampled triplets satisfying AP < AN. These results demonstrate that FlowDelayNet provides an automated and robust solution for flow-delay estimation across heterogeneous field conditions.
Keywords
metric learning, precision agriculture, self-supervised learning, siamese learning, yield monitoring
Publication Date
9-3-2026
Publication Title
Computers and Electronics in Agriculture
Publisher
Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Rights
© 2026 The Author(s)
Recommended Citation
Uddin, M., Samiappan, S., & Drewry, J. L. (2026). FlowDelayNet: Self-supervised siamese learning for automated flow-delay estimation in yield monitor data. Computers and Electronics in Agriculture, 255, 112365. https://doi.org/10.1016/j.compag.2026.112365
