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Precision Agriculture
Doing the right thing in the right place, at the right dose.
Overview
Precision agriculture collects data (images, sensors, yield maps) so that inputs such as herbicide, water or fertiliser can be applied only where and as much as needed. Data collection is only worthwhile if it ends in an intervention.
Key concepts
- Site-specific management
- Treat the field as a map of different zones, not as one uniform unit.
- Variable-rate application
- Sprayers, cultivators and spraying drones change dose or switch on and off by location, driven by a prescription map or shapefile.
- Weed pressure
- Weed plants per square metre; a heat map of weed pressure tells the machine where to act.
- Worked example
- In our UAV spot-spraying trial herbicide was needed on 32.43 % of the field, so 100 − 32.43 = 67.57 % of the herbicide area was saved compared with full-area spraying.
Case study
Case studies from my work: weed pressure maps without training data, and an end-to-end workflow from detection to sprayer zones.
References from my work
2025Q1
Aerial Image-Based Crop Row Detection and Weed Pressure Mapping Method
L. Moldvai, P. Á. Mesterházi, G. Teschner, A. Nyéki
Agronomy 2025, 15(8), 1762
Abstract
A six-phase, feature-based computer vision method that detects crop rows and generates weed pressure maps from UAV images taken at 20–30 m, without training data or deep learning. Tested on 208 images and 120 artificial datasets; the in-row concentration ratio (IRCR) tells whether a result is usable. With 678 crop plants and 600 weeds the method reached F1 = 86.32 %, and the weed-to-crop ratio correlated 98–99 % with ground truth.
DOI: 10.3390/agronomy15081762
2026conference proceedings, not refereed
UAV-Based Detection and Precision Management of Cirsium arvense: An End-to-End Workflow from Deep Learning to Variable-Rate Spraying
L. Moldvai, P. Á. Mesterházi, G. Teschner, A. Nyéki
Proceedings of the 17th International Conference on Precision Agriculture (ICPA) and 11th Brazilian Congress on Precision and Digital Agriculture, Porto Alegre, Brazil, 13–15 July 2026
Abstract
An end-to-end workflow for detecting creeping thistle (Cirsium arvense) in UAV imagery and turning the detections into sprayer-compatible management zones. A single-class YOLO model trained on 800 annotated tiles (augmented to 13,280 images) reached mAP@0.5 = 78.97 % and F1 = 77.2 %. Herbicide was needed on only 32.43 % of the field, a 67.57 % reduction compared with full-area spraying.
2024Hungarian-language article
Paradicsom hozamelőrejelzés technológiai lépései gépi látás segítségével
L. Moldvai, B. Ambrus, G. Teschner, A. Nyéki
Acta Agronomica Óváriensis 2024, 65(1)
Abstract
(Hungarian-language article.) Technological steps of tomato yield forecasting with machine vision: which data image processing can provide in precision crop production and how they support yield, harvest-time and stress estimation.
DOI: 10.17108/ActAgrOvar.2024.65.1.89