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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/21/4486 |
| _version_ | 1797511846856491008 |
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| author | Ildar Rakhmatulin Andreas Kamilaris Christian Andreasen |
| author_facet | Ildar Rakhmatulin Andreas Kamilaris Christian Andreasen |
| author_sort | Ildar Rakhmatulin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:52:47Z |
| format | Article |
| id | doaj.art-47f51e3e92cc450795f87e68b1b9d84b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:52:47Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-47f51e3e92cc450795f87e68b1b9d84b2023-11-22T21:34:10ZengMDPI AGRemote Sensing2072-42922021-11-011321448610.3390/rs13214486Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A ReviewIldar Rakhmatulin0Andreas Kamilaris1Christian Andreasen2Department of Power Plant Networks and Systems, South Ural State University, 454080 Chelyabinsk City, RussiaCYENS Center of Excellence, Dimarchias Square 23, Nicosia 1016, CyprusDepartment of Plant and Environmental Sciences, University of Copenhagen, Højbakkegaard Allé 13, DK 2630 Taastrup, DenmarkAutomation, 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.https://www.mdpi.com/2072-4292/13/21/4486deep learning in agricultureprecision agricultureweed detectionrobotic weed controlmachine vision for weed control |
| spellingShingle | Ildar Rakhmatulin Andreas Kamilaris Christian Andreasen Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review Remote Sensing deep learning in agriculture precision agriculture weed detection robotic weed control machine vision for weed control |
| title | 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_short | 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 |
| topic | deep learning in agriculture precision agriculture weed detection robotic weed control machine vision for weed control |
| url | https://www.mdpi.com/2072-4292/13/21/4486 |
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