Cargando…
Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed typ...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8660967/ https://www.ncbi.nlm.nih.gov/pubmed/34899768 http://dx.doi.org/10.3389/fpls.2021.702626 |
_version_ | 1784613300400553984 |
---|---|
author | Zhang, Jing Maleski, Jerome Jespersen, David Waltz, F. C. Rains, Glen Schwartz, Brian |
author_facet | Zhang, Jing Maleski, Jerome Jespersen, David Waltz, F. C. Rains, Glen Schwartz, Brian |
author_sort | Zhang, Jing |
collection | PubMed |
description | Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, grass weeds, spurge, sedge, and no weeds at a precision between 0.68 and 0.87, 0.57 and 0.82, 0.68 and 0.83, 0.66 and 0.90, and 0.80 and 0.88, respectively, when using UAS images at 0.57 cm–1.28 cm pixel(–1) resolution. Recall ranges for the five classes were 0.78–0.93, 0.65–0.87, 0.82–0.93, 0.52–0.79, and 0.94–0.99. Additionally, this study demonstrates that a CNN can achieve precision and recall above 0.9 at detecting different types of weeds during turf establishment when the weeds are mature. The CNN is limited by the image resolution, and more than one model may be needed in practice to improve the overall performance of weed mapping. |
format | Online Article Text |
id | pubmed-8660967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86609672021-12-11 Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network Zhang, Jing Maleski, Jerome Jespersen, David Waltz, F. C. Rains, Glen Schwartz, Brian Front Plant Sci Plant Science Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, grass weeds, spurge, sedge, and no weeds at a precision between 0.68 and 0.87, 0.57 and 0.82, 0.68 and 0.83, 0.66 and 0.90, and 0.80 and 0.88, respectively, when using UAS images at 0.57 cm–1.28 cm pixel(–1) resolution. Recall ranges for the five classes were 0.78–0.93, 0.65–0.87, 0.82–0.93, 0.52–0.79, and 0.94–0.99. Additionally, this study demonstrates that a CNN can achieve precision and recall above 0.9 at detecting different types of weeds during turf establishment when the weeds are mature. The CNN is limited by the image resolution, and more than one model may be needed in practice to improve the overall performance of weed mapping. Frontiers Media S.A. 2021-11-26 /pmc/articles/PMC8660967/ /pubmed/34899768 http://dx.doi.org/10.3389/fpls.2021.702626 Text en Copyright © 2021 Zhang, Maleski, Jespersen, Waltz, Rains and Schwartz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Zhang, Jing Maleski, Jerome Jespersen, David Waltz, F. C. Rains, Glen Schwartz, Brian Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network |
title | Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network |
title_full | Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network |
title_fullStr | Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network |
title_full_unstemmed | Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network |
title_short | Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network |
title_sort | unmanned aerial system-based weed mapping in sod production using a convolutional neural network |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8660967/ https://www.ncbi.nlm.nih.gov/pubmed/34899768 http://dx.doi.org/10.3389/fpls.2021.702626 |
work_keys_str_mv | AT zhangjing unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT maleskijerome unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT jespersendavid unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT waltzfc unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT rainsglen unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT schwartzbrian unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork |