A Review of CNN Applications in Smart Agriculture Using Multimodal Data
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/472 |
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| author | Mohammad El Sakka Mihai Ivanovici Lotfi Chaari Josiane Mothe |
| author_facet | Mohammad El Sakka Mihai Ivanovici Lotfi Chaari Josiane Mothe |
| author_sort | Mohammad El Sakka |
| collection | DOAJ |
| description | This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available. |
| format | Article |
| id | doaj-art-87b00df90e114121aa0323c5f2d0a30f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-87b00df90e114121aa0323c5f2d0a30f2025-01-24T13:49:03ZengMDPI AGSensors1424-82202025-01-0125247210.3390/s25020472A Review of CNN Applications in Smart Agriculture Using Multimodal DataMohammad El Sakka0Mihai Ivanovici1Lotfi Chaari2Josiane Mothe3Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, FranceDepartment of Electronics and Computers, Transilvania University of Brasov, 500036 Brasov, RomaniaInstitut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, FranceInstitut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, FranceThis review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available.https://www.mdpi.com/1424-8220/25/2/472convolutional neural networksmart agricultureweed detectioncrop disease detectioncrop classificationyield prediction |
| spellingShingle | Mohammad El Sakka Mihai Ivanovici Lotfi Chaari Josiane Mothe A Review of CNN Applications in Smart Agriculture Using Multimodal Data Sensors convolutional neural network smart agriculture weed detection crop disease detection crop classification yield prediction |
| title | A Review of CNN Applications in Smart Agriculture Using Multimodal Data |
| title_full | A Review of CNN Applications in Smart Agriculture Using Multimodal Data |
| title_fullStr | A Review of CNN Applications in Smart Agriculture Using Multimodal Data |
| title_full_unstemmed | A Review of CNN Applications in Smart Agriculture Using Multimodal Data |
| title_short | A Review of CNN Applications in Smart Agriculture Using Multimodal Data |
| title_sort | review of cnn applications in smart agriculture using multimodal data |
| topic | convolutional neural network smart agriculture weed detection crop disease detection crop classification yield prediction |
| url | https://www.mdpi.com/1424-8220/25/2/472 |
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