Precision agriculture for iceberg lettuce: From spatial sensing to per plant decision making and control
Precision agriculture enables growers to sense growth in the field and take actions at a high resolution. In this context, agriculture can be considered a feedback system, and feedback control algorithms are a promising approach for decision-making at a per-plant level. We present a control approach to assign per-plant nitrogen prescriptions for iceberg lettuce, with the objective of reducing variability at harvest and, as a result, increasing yield. The per-plant nitrogen inputs were generated using a proportional consensus-protocol style controller and non-destructive measurements of growth. This resulted in smaller lettuce plants receiving an increased dose of nitrogen and oversized plants receiving a decreased dose. In this paper, we present the results of three in-field trials applying the controller to real plants with manual applications. Our results demonstrated a reduction in the plant mass variance of outdoor crop in two of our three farm trials (32.6% and 19.7% lower variance in comparison to cohorts with a typical approach). This is a promising result, which could be improved in future work by developing a more accurate proxy measurement for crop growth and accounting for soil nitrogen. • In iceberg lettuce and other high value crop, variability at harvest reduces yield. • The field can be considered as a multi-agent system where each plant is an agent. • A consensus protocol can generate per-plant nitrogen doses to reduce crop variation. • This is model-free and requires only relative measurements of crop growth.
Authors
- William Rohde
- Fulvio Forni (ORCID: https://orcid.org/0000-0002-5728-0176)
Institutions
- University of Cambridge (GB)
- Bridge University (SS)
Publication Details
- Journal
- Smart Agricultural Technology
- Published
- 2025-01-22
- DOI
- https://doi.org/10.1016/j.atech.2025.100797
- Citations
- 2
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 3.43