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Towards practical object detection for weed spraying in precision agriculture
Weeds pose a persistent threat to farmers’ yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657197/ https://www.ncbi.nlm.nih.gov/pubmed/38023838 http://dx.doi.org/10.3389/fpls.2023.1183277 |
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author | Darbyshire, Madeleine Salazar-Gomez, Adrian Gao, Junfeng Sklar, Elizabeth I. Parsons, Simon |
author_facet | Darbyshire, Madeleine Salazar-Gomez, Adrian Gao, Junfeng Sklar, Elizabeth I. Parsons, Simon |
author_sort | Darbyshire, Madeleine |
collection | PubMed |
description | Weeds pose a persistent threat to farmers’ yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying the entire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds. |
format | Online Article Text |
id | pubmed-10657197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106571972023-01-01 Towards practical object detection for weed spraying in precision agriculture Darbyshire, Madeleine Salazar-Gomez, Adrian Gao, Junfeng Sklar, Elizabeth I. Parsons, Simon Front Plant Sci Plant Science Weeds pose a persistent threat to farmers’ yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying the entire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds. Frontiers Media S.A. 2023-11-03 /pmc/articles/PMC10657197/ /pubmed/38023838 http://dx.doi.org/10.3389/fpls.2023.1183277 Text en Copyright © 2023 Darbyshire, Salazar-Gomez, Gao, Sklar and Parsons 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 Darbyshire, Madeleine Salazar-Gomez, Adrian Gao, Junfeng Sklar, Elizabeth I. Parsons, Simon Towards practical object detection for weed spraying in precision agriculture |
title | Towards practical object detection for weed spraying in precision agriculture |
title_full | Towards practical object detection for weed spraying in precision agriculture |
title_fullStr | Towards practical object detection for weed spraying in precision agriculture |
title_full_unstemmed | Towards practical object detection for weed spraying in precision agriculture |
title_short | Towards practical object detection for weed spraying in precision agriculture |
title_sort | towards practical object detection for weed spraying in precision agriculture |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657197/ https://www.ncbi.nlm.nih.gov/pubmed/38023838 http://dx.doi.org/10.3389/fpls.2023.1183277 |
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