Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted...

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Autores principales:	Ildar Rakhmatuiln, Andreas Kamilaris, Christian Andreasen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning in agriculture precision agriculture weed detection robotic weed control machine vision for weed control Science Q
Acceso en línea:	https://doaj.org/article/47f51e3e92cc450795f87e68b1b9d84b
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spelling	oai:doaj.org-article:47f51e3e92cc450795f87e68b1b9d84b2021-11-11T18:59:12ZDeep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review10.3390/rs132144862072-4292https://doaj.org/article/47f51e3e92cc450795f87e68b1b9d84b2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4486https://doaj.org/toc/2072-4292Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.Ildar RakhmatuilnAndreas KamilarisChristian AndreasenMDPI AGarticledeep learning in agricultureprecision agricultureweed detectionrobotic weed controlmachine vision for weed controlScienceQENRemote Sensing, Vol 13, Iss 4486, p 4486 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning in agriculture precision agriculture weed detection robotic weed control machine vision for weed control Science Q
spellingShingle	deep learning in agriculture precision agriculture weed detection robotic weed control machine vision for weed control Science Q Ildar Rakhmatuiln Andreas Kamilaris Christian Andreasen Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
description	Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.
format	article
author	Ildar Rakhmatuiln Andreas Kamilaris Christian Andreasen
author_facet	Ildar Rakhmatuiln Andreas Kamilaris Christian Andreasen
author_sort	Ildar Rakhmatuiln
title	Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
title_short	Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
title_full	Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
title_fullStr	Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
title_full_unstemmed	Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
title_sort	deep neural networks to detect weeds from crops in agricultural environments in real-time: a review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/47f51e3e92cc450795f87e68b1b9d84b
work_keys_str_mv	AT ildarrakhmatuiln deepneuralnetworkstodetectweedsfromcropsinagriculturalenvironmentsinrealtimeareview AT andreaskamilaris deepneuralnetworkstodetectweedsfromcropsinagriculturalenvironmentsinrealtimeareview AT christianandreasen deepneuralnetworkstodetectweedsfromcropsinagriculturalenvironmentsinrealtimeareview
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Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

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