Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning
This systematic literature review analyzes 43 scientific studies on the application of deep learning (DL) techniques for the early detection of diseases in cocoa (Theobroma cacao). Using the PRISMA methodology and the PICOC framework, models and architectures applied to classification, detection, an...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| _version_ | 1881700520512454656 |
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| author | Yomer Ysidro-Santos Andy Roel Vasquez-Bueno Isait Camizán-Ojeda |
| author_browse | Andy Roel Vasquez-Bueno Isait Camizán-Ojeda Yomer Ysidro-Santos |
| author_facet | Yomer Ysidro-Santos Andy Roel Vasquez-Bueno Isait Camizán-Ojeda |
| author_sort | Yomer Ysidro-Santos |
| collection | Producción científica |
| description | This systematic literature review analyzes 43 scientific studies on the application of deep learning (DL) techniques for the early detection of diseases in cocoa (Theobroma cacao). Using the PRISMA methodology and the PICOC framework, models and architectures applied to classification, detection, and segmentation tasks were examined. The results show that lightweight models from the YOLO family achieve accuracy rates of up to 96% in cocoa disease identification, with the capability to perform real-time inference. Likewise, Vision Transformer (ViT) and U-Net architectures achieve accuracy rates ranging from 97% to 99% in controlled environments and related agricultural datasets, demonstrating their potential for transfer learning applications. However, limitations remain regarding cross-domain generalization, model explainability, and the availability of data collected under real-world field conditions. It is concluded that future research should expand public datasets, incorporate explainable artificial intelligence (XAI) and self-supervised learning (SSL), and optimize models for resource-constrained devices, thereby promoting sustainable and scalable solutions for smart cocoa cultivation. |
| format | Article |
| id | openalex:W7212085531 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | openalex:W7212085531Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learningYomer Ysidro-SantosAndy Roel Vasquez-BuenoIsait Camizán-OjedaTheobroma cacaofungal plant diseasecomputer visiondeep learning architecturesYOLOvision transformerU-Netdisease detectionplant disease classificationtransfer learningexplainable AIself-supervised learninglightweight modelssmart agriculturecacao cultivationSmart Agriculture and AIAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect DetectionThis systematic literature review analyzes 43 scientific studies on the application of deep learning (DL) techniques for the early detection of diseases in cocoa (Theobroma cacao). Using the PRISMA methodology and the PICOC framework, models and architectures applied to classification, detection, and segmentation tasks were examined. The results show that lightweight models from the YOLO family achieve accuracy rates of up to 96% in cocoa disease identification, with the capability to perform real-time inference. Likewise, Vision Transformer (ViT) and U-Net architectures achieve accuracy rates ranging from 97% to 99% in controlled environments and related agricultural datasets, demonstrating their potential for transfer learning applications. However, limitations remain regarding cross-domain generalization, model explainability, and the availability of data collected under real-world field conditions. It is concluded that future research should expand public datasets, incorporate explainable artificial intelligence (XAI) and self-supervised learning (SSL), and optimize models for resource-constrained devices, thereby promoting sustainable and scalable solutions for smart cocoa cultivation.Articlehttps://doi.org/10.55873/kt5n2q37https://revistas.unamad.edu.pe/index.php/rad/article/download/417/792W7212085531enRevista Amazonía Digital2810-87012026-07-25 |
| spellingShingle | Theobroma cacao fungal plant disease computer vision deep learning architectures YOLO vision transformer U-Net disease detection plant disease classification transfer learning explainable AI self-supervised learning lightweight models smart agriculture cacao cultivation Smart Agriculture and AI Advanced Neural Network Applications Industrial Vision Systems and Defect Detection Yomer Ysidro-Santos Andy Roel Vasquez-Bueno Isait Camizán-Ojeda Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning |
| title | Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning |
| title_full | Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning |
| title_fullStr | Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning |
| title_full_unstemmed | Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning |
| title_short | Visión por computadora para detectar enfermedades fúngicas en cacao: revisión sistemática de arquitecturas de deep learning |
| title_sort | vision por computadora para detectar enfermedades fungicas en cacao revision sistematica de arquitecturas de deep learning |
| topic | Theobroma cacao fungal plant disease computer vision deep learning architectures YOLO vision transformer U-Net disease detection plant disease classification transfer learning explainable AI self-supervised learning lightweight models smart agriculture cacao cultivation Smart Agriculture and AI Advanced Neural Network Applications Industrial Vision Systems and Defect Detection |
| topic_facet | Theobroma cacao fungal plant disease computer vision deep learning architectures YOLO vision transformer U-Net disease detection plant disease classification transfer learning explainable AI self-supervised learning lightweight models smart agriculture cacao cultivation Smart Agriculture and AI Advanced Neural Network Applications Industrial Vision Systems and Defect Detection |
| url | https://doi.org/10.55873/kt5n2q37 https://revistas.unamad.edu.pe/index.php/rad/article/download/417/792 |
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