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Towards deep learning based smart farming for intelligent weeds management in crops

INTRODUCTION: Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve producti...

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Autores principales: Saqib, Muhammad Ali, Aqib, Muhammad, Tahir, Muhammad Naveed, Hafeez, Yaser
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416644/
https://www.ncbi.nlm.nih.gov/pubmed/37575940
http://dx.doi.org/10.3389/fpls.2023.1211235
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author Saqib, Muhammad Ali
Aqib, Muhammad
Tahir, Muhammad Naveed
Hafeez, Yaser
author_facet Saqib, Muhammad Ali
Aqib, Muhammad
Tahir, Muhammad Naveed
Hafeez, Yaser
author_sort Saqib, Muhammad Ali
collection PubMed
description INTRODUCTION: Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops. METHODS: Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny). RESULTS AND DISCUSSION: The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value. FUTURE WORK: In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.
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spelling pubmed-104166442023-08-12 Towards deep learning based smart farming for intelligent weeds management in crops Saqib, Muhammad Ali Aqib, Muhammad Tahir, Muhammad Naveed Hafeez, Yaser Front Plant Sci Plant Science INTRODUCTION: Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops. METHODS: Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny). RESULTS AND DISCUSSION: The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value. FUTURE WORK: In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time. Frontiers Media S.A. 2023-07-28 /pmc/articles/PMC10416644/ /pubmed/37575940 http://dx.doi.org/10.3389/fpls.2023.1211235 Text en Copyright © 2023 Saqib, Aqib, Tahir and Hafeez 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
Saqib, Muhammad Ali
Aqib, Muhammad
Tahir, Muhammad Naveed
Hafeez, Yaser
Towards deep learning based smart farming for intelligent weeds management in crops
title Towards deep learning based smart farming for intelligent weeds management in crops
title_full Towards deep learning based smart farming for intelligent weeds management in crops
title_fullStr Towards deep learning based smart farming for intelligent weeds management in crops
title_full_unstemmed Towards deep learning based smart farming for intelligent weeds management in crops
title_short Towards deep learning based smart farming for intelligent weeds management in crops
title_sort towards deep learning based smart farming for intelligent weeds management in crops
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416644/
https://www.ncbi.nlm.nih.gov/pubmed/37575940
http://dx.doi.org/10.3389/fpls.2023.1211235
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