A perception-guided CNN for grape bunch detection
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| Title: | A perception-guided CNN for grape bunch detection |
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| Authors: | Bruni V., Dominijanni G., Vitulano D., Ramella G. |
| Source: | IMACS 2023-21st IMACS World Congress, Roma, 11-15/09/2023 info:cnr-pdr/source/autori:Bruni V, Dominijanni G, Vitulano D, Ramella G/congresso_nome:IMACS 2023-21st IMACS World Congress/congresso_luogo:Roma/congresso_data:11-15%2F09%2F2023/anno:2023/pagina_da:/pagina_a:/intervallo_pagine |
| Publisher Information: | Elsevier BV, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Grape Bunch Detection, Pixel-wise classification, Biology and other natural sciences, visual contrast, convolutional neural network, Smart Farming, CNN, bunch detection, Visual contrast, Convolutional Neural Network, color opponents, 04 agricultural and veterinary sciences, 02 engineering and technology, Computer science, pixel-wise classification, Color opponent, Precision Viticulture, 0202 electrical engineering, electronic engineering, information engineering, Grape bunch detection, Human Perception of Visual Information, 0401 agriculture, forestry, and fisheries, grape bunch detection, precision viticulture, Color opponents |
| Description: | Smart farming is becoming an active and interdisciplinary research field as it requires to solve interesting and challenging research issues to respond concretely to the demands of specific use-cases. One of the most delicate tasks is the automatic yield estimation, as for example in vineyards [1]. Computer vision methods that implement the rules of the human visual system can contribute to task accomplishment as they simulate what winemakers make manually [2]. An automatic artificial-intelligence method for grape bunch detection from RGB images is presented. It properly defines the input of a Convolutional Neural Network whose task is the segmentation of grape bunches [3]. The network input consists of pointwise visual contrast-based measurements that allow us to discriminate and detect grape bunches even in uncontrolled acquisition conditions and with limited computational load. The latter property makes the proposed method implementable on smart devices and appropriate for onsite and real-time applications. |
| Document Type: | Article Conference object |
| File Description: | application/xml; application/pdf |
| Language: | English |
| ISSN: | 0378-4754 |
| DOI: | 10.1016/j.matcom.2024.11.004 |
| Access URL: | https://zbmath.org/8031633 https://doi.org/10.1016/j.matcom.2024.11.004 http://www.cnr.it/prodotto/i/490695 https://publications.cnr.it/doc/490695 https://hdl.handle.net/11573/1704893 https://hdl.handle.net/11573/1727560 https://doi.org/10.1016/j.matcom.2024.11.004 |
| Rights: | CC BY CC BY NC ND |
| Accession Number: | edsair.doi.dedup.....afdc42b46c4d480db22e97efaab3731d |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Smart farming is becoming an active and interdisciplinary research field as it requires to solve interesting and challenging research issues to respond concretely to the demands of specific use-cases. One of the most delicate tasks is the automatic yield estimation, as for example in vineyards [1]. Computer vision methods that implement the rules of the human visual system can contribute to task accomplishment as they simulate what winemakers make manually [2]. An automatic artificial-intelligence method for grape bunch detection from RGB images is presented. It properly defines the input of a Convolutional Neural Network whose task is the segmentation of grape bunches [3]. The network input consists of pointwise visual contrast-based measurements that allow us to discriminate and detect grape bunches even in uncontrolled acquisition conditions and with limited computational load. The latter property makes the proposed method implementable on smart devices and appropriate for onsite and real-time applications. |
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| ISSN: | 03784754 |
| DOI: | 10.1016/j.matcom.2024.11.004 |