Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why
Artificial Neural Networks (ANNs) based models have emerged as a powerful tool for solving complex nonlinear problems in agriculture. These models simulate the human nervous system’s structure, allowing them to learn hierarchical features from the data and solve nonlinear problems efficiently. Des...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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Elsevier
2025
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| Acceso en línea: | https://hdl.handle.net/20.500.11939/9037 https://www.sciencedirect.com/science/article/pii/S0168169925000444 |
| _version_ | 1855492611678142464 |
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| author | Castillo-Gironés, Salvador Munera, Sandra Martinez-Sober, Marcelino Blasco, José Cubero, Sergio Gómez-Sanchis, Juan |
| author_browse | Blasco, José Castillo-Gironés, Salvador Cubero, Sergio Gómez-Sanchis, Juan Martinez-Sober, Marcelino Munera, Sandra |
| author_facet | Castillo-Gironés, Salvador Munera, Sandra Martinez-Sober, Marcelino Blasco, José Cubero, Sergio Gómez-Sanchis, Juan |
| author_sort | Castillo-Gironés, Salvador |
| collection | ReDivia |
| description | Artificial Neural Networks (ANNs) based models have emerged as a powerful tool for solving complex nonlinear
problems in agriculture. These models simulate the human nervous system’s structure, allowing them to learn
hierarchical features from the data and solve nonlinear problems efficiently. Despite requiring a large amount of
training data, ANNs with shallow architectures demonstrate superior performance in extracting relevant features
and establishing accurate models, instilling confidence in their effectiveness compared to conventional machine
learning methods. The versatility of ANNs enables their application in various agricultural domains, including
precision agriculture, species classification, phenotyping, and food quality and safety assessment. ANNs combined
with image analysis have proven valuable in disease detection, plant phenotyping, and fruit quality
evaluation. The use of deep learning in agriculture has experienced exponential growth, as evident from the
increasing number of publications in recent years. This article overviews recent advancements in applying ANNs
in agriculture. It delves into the fundamental principles behind various types of agricultural data and ANN
models, discussing their benefits and challenges. The article offers valuable insights into the proper use and
functioning of each neural network, data processing for improved model outcomes, and the diverse applications
of ANNs in the agricultural sector. It aims to equip readers with practical information on data utilisation, model
selection based on data type, functionality, and current research applications |
| format | Artículo |
| id | ReDivia9037 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia90372025-04-25T14:49:51Z Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why Castillo-Gironés, Salvador Munera, Sandra Martinez-Sober, Marcelino Blasco, José Cubero, Sergio Gómez-Sanchis, Juan Deep Learning Applications N01 Agricultural engineering Quality Agricultural products Neural networks Artificial Neural Networks (ANNs) based models have emerged as a powerful tool for solving complex nonlinear problems in agriculture. These models simulate the human nervous system’s structure, allowing them to learn hierarchical features from the data and solve nonlinear problems efficiently. Despite requiring a large amount of training data, ANNs with shallow architectures demonstrate superior performance in extracting relevant features and establishing accurate models, instilling confidence in their effectiveness compared to conventional machine learning methods. The versatility of ANNs enables their application in various agricultural domains, including precision agriculture, species classification, phenotyping, and food quality and safety assessment. ANNs combined with image analysis have proven valuable in disease detection, plant phenotyping, and fruit quality evaluation. The use of deep learning in agriculture has experienced exponential growth, as evident from the increasing number of publications in recent years. This article overviews recent advancements in applying ANNs in agriculture. It delves into the fundamental principles behind various types of agricultural data and ANN models, discussing their benefits and challenges. The article offers valuable insights into the proper use and functioning of each neural network, data processing for improved model outcomes, and the diverse applications of ANNs in the agricultural sector. It aims to equip readers with practical information on data utilisation, model selection based on data type, functionality, and current research applications 2025-03-11T08:17:10Z 2025-03-11T08:17:10Z 2025 article publishedVersion Castillo-Girones, S., Munera, S., Martínez-Sober, M., Blasco, J., Cubero, S., & Gómez-Sanchis, J. (2025). Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why. Computers and Electronics in Agriculture, 230, 109938. 2589 - 7217 https://hdl.handle.net/20.500.11939/9037 10.1016/j.compag.2025.109938 https://www.sciencedirect.com/science/article/pii/S0168169925000444 en This work was partially funded through the projects MICIU AEI PID2023-150192OR-C31, C32 and C-33 with the support of FEDER funds, project AEI TED2021-130117B-C31 and C33, by the project GVA- PROMETEO CIPROM/2021/014. Salvador Castillo thanks INIA for the FPI-INIA grant number PRE2020-094491, partially supported by Euro pean Union FSE funds. Sandra Munera expresses her thanks for the postdoctoral contract Juan de la Cierva - Formaci´on (FJC2021-047786- I) co-funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. info:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2023-150192OR-C32/Automatización de la inspección de la calidad interna y seguridad de frutas en linea, utilizando imagen hiperspectral VIS-NIR e inteligencia artificial Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Elsevier electronico |
| spellingShingle | Deep Learning Applications N01 Agricultural engineering Quality Agricultural products Neural networks Castillo-Gironés, Salvador Munera, Sandra Martinez-Sober, Marcelino Blasco, José Cubero, Sergio Gómez-Sanchis, Juan Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why |
| title | Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why |
| title_full | Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why |
| title_fullStr | Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why |
| title_full_unstemmed | Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why |
| title_short | Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why |
| title_sort | artificial neural networks in agriculture the core of artificial intelligence what when and why |
| topic | Deep Learning Applications N01 Agricultural engineering Quality Agricultural products Neural networks |
| url | https://hdl.handle.net/20.500.11939/9037 https://www.sciencedirect.com/science/article/pii/S0168169925000444 |
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