Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review
Abstract In recent years, machine vision, deep learning, and artificial intelligence have garnered significant research interest in precision agriculture. This article aims to provide a comprehensive review of the latest advancements in machine vision application in tomato cultivation. This study ex...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07613-x |
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| author | L. Moldvai A. Nyéki |
| author_facet | L. Moldvai A. Nyéki |
| author_sort | L. Moldvai |
| collection | DOAJ |
| description | Abstract In recent years, machine vision, deep learning, and artificial intelligence have garnered significant research interest in precision agriculture. This article aims to provide a comprehensive review of the latest advancements in machine vision application in tomato cultivation. This study explores integrating cognitive technologies in agriculture, particularly in tomato production. The review covers various studies on tomatoes and machine vision that support tomato harvesting, such as classification, fruit counting, and yield estimation. It addresses plant health monitoring approaches, including detecting weeds, pests, leaf diseases, and fruit disorders. The paper also examines the latest research efforts in vehicle navigation systems and tomato-harvesting robots. The primary objective of this article was to present a thorough analysis of the image processing algorithms utilized in research over the past two years, along with their outcomes. |
| format | Article |
| id | doaj-art-3c9914d4fc8f4d06bd0ae4d6f35e83d2 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-3c9914d4fc8f4d06bd0ae4d6f35e83d22025-08-24T11:45:02ZengSpringerDiscover Applied Sciences3004-92612025-08-017915610.1007/s42452-025-07613-xInnovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a reviewL. Moldvai0A. Nyéki1Department of Biosystems Engineering and Precision Technology, Albert Kázmér Mosonmagyaróvár Faculty of Agricultural and Food Sciences, Széchenyi István UniversityDepartment of Biosystems Engineering and Precision Technology, Albert Kázmér Mosonmagyaróvár Faculty of Agricultural and Food Sciences, Széchenyi István UniversityAbstract In recent years, machine vision, deep learning, and artificial intelligence have garnered significant research interest in precision agriculture. This article aims to provide a comprehensive review of the latest advancements in machine vision application in tomato cultivation. This study explores integrating cognitive technologies in agriculture, particularly in tomato production. The review covers various studies on tomatoes and machine vision that support tomato harvesting, such as classification, fruit counting, and yield estimation. It addresses plant health monitoring approaches, including detecting weeds, pests, leaf diseases, and fruit disorders. The paper also examines the latest research efforts in vehicle navigation systems and tomato-harvesting robots. The primary objective of this article was to present a thorough analysis of the image processing algorithms utilized in research over the past two years, along with their outcomes.https://doi.org/10.1007/s42452-025-07613-xComputer vision (CV)Convolution neural networks (CNN)Vision transformers (ViT)Deep learning (DL)Tomato detection and cultivation |
| spellingShingle | L. Moldvai A. Nyéki Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review Discover Applied Sciences Computer vision (CV) Convolution neural networks (CNN) Vision transformers (ViT) Deep learning (DL) Tomato detection and cultivation |
| title | Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review |
| title_full | Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review |
| title_fullStr | Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review |
| title_full_unstemmed | Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review |
| title_short | Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review |
| title_sort | innovative computer vision methods for tomato solanum lycopersicon detection and cultivation a review |
| topic | Computer vision (CV) Convolution neural networks (CNN) Vision transformers (ViT) Deep learning (DL) Tomato detection and cultivation |
| url | https://doi.org/10.1007/s42452-025-07613-x |
| work_keys_str_mv | AT lmoldvai innovativecomputervisionmethodsfortomatosolanumlycopersicondetectionandcultivationareview AT anyeki innovativecomputervisionmethodsfortomatosolanumlycopersicondetectionandcultivationareview |