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Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field
In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782522/ https://www.ncbi.nlm.nih.gov/pubmed/36560092 http://dx.doi.org/10.3390/s22249723 |
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author | Sanchez, Paolo Rommel Zhang, Hong |
author_facet | Sanchez, Paolo Rommel Zhang, Hong |
author_sort | Sanchez, Paolo Rommel |
collection | PubMed |
description | In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations, especially in unstructured working environments such as broadcast-seeded fields. In this study, we developed a modular precision sprayer by distributing the high computational load of CNN among parallel low-cost and low-power vision computing devices. The sprayer utilized a custom precision spraying algorithm based on SSD-MobileNetV1 running on a Jetson Nano 4 GB. The model achieved 76% [Formula: see text] at 19 fps for weed and soybean detection in a broadcast-seeded field. Further, the sprayer targeted all weed samples and exhibited up to 48.89% spray volume reduction with a typical walking speed up to 3.0 km/h, which was three times faster than similar systems with known targeting performance. With these results, the study demonstrated that CNN-based precision spraying in a complex broadcast-seeded field can achieve increased velocity at high accuracy without needing powerful and expensive computational hardware using modular designs. |
format | Online Article Text |
id | pubmed-9782522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97825222022-12-24 Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field Sanchez, Paolo Rommel Zhang, Hong Sensors (Basel) Article In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations, especially in unstructured working environments such as broadcast-seeded fields. In this study, we developed a modular precision sprayer by distributing the high computational load of CNN among parallel low-cost and low-power vision computing devices. The sprayer utilized a custom precision spraying algorithm based on SSD-MobileNetV1 running on a Jetson Nano 4 GB. The model achieved 76% [Formula: see text] at 19 fps for weed and soybean detection in a broadcast-seeded field. Further, the sprayer targeted all weed samples and exhibited up to 48.89% spray volume reduction with a typical walking speed up to 3.0 km/h, which was three times faster than similar systems with known targeting performance. With these results, the study demonstrated that CNN-based precision spraying in a complex broadcast-seeded field can achieve increased velocity at high accuracy without needing powerful and expensive computational hardware using modular designs. MDPI 2022-12-12 /pmc/articles/PMC9782522/ /pubmed/36560092 http://dx.doi.org/10.3390/s22249723 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sanchez, Paolo Rommel Zhang, Hong Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field |
title | Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field |
title_full | Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field |
title_fullStr | Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field |
title_full_unstemmed | Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field |
title_short | Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field |
title_sort | evaluation of a cnn-based modular precision sprayer in broadcast-seeded field |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782522/ https://www.ncbi.nlm.nih.gov/pubmed/36560092 http://dx.doi.org/10.3390/s22249723 |
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