Pedro José de Melo Teixeira Pinto Integrated Member

CV Ciência Vitae

BIO

Name: Pedro José de Melo Teixeira Pinto

Aggregation: Integrated Member

Scopus author ID: 56538928200

Orcid ID: 0000-0001-8257-0143

Ciência Vitae: 9011-BFDC-7EDC

Email: pmelo@utad.pt

Phone: 259350753

Research Groups

Technological innovations to support agrarian systems

Publications

Fuzziness measure approach to automatic histogram threshold

Lopes N.V., Bustince H., Filipe V., Pinto P.M., (2026) Fuzziness measure approach to automatic histogram threshold Computational Vision and Medical Image Processing Vipimage 2007 :295-299. doi: 10.1201/9781003759805-55.

Early detection of downy mildew in vineyards using deep neural networks for semantic segmentation

Hernández I., Silva R., Melo-Pinto P., Gutiérrez S., Tardaguila J., (2025) Early detection of downy mildew in vineyards using deep neural networks for semantic segmentation Biosystems Engineering 252 :15-31. doi: 10.1016/j.biosystemseng.2025.02.007.

Multimodal object detection: an architecture using feature-level fusion and deep learning

Silva R., Coelho E., Pimenta N., Durães D., Alves V., Bandeira L., Machado J., Novais P., Melo-Pinto P., (2025) Multimodal object detection: an architecture using feature-level fusion and deep learning Neural Computing and Applications 37 :23799-23810. ISSN: 14333058. doi: 10.1007/s00521-025-11521-x.

Evaluating the generalization ability of deep learning models: An application on sugar content estimation from hyperspectral images of wine grape berries

Silva R., Freitas O., Melo-Pinto P., (2024) Evaluating the generalization ability of deep learning models: An application on sugar content estimation from hyperspectral images of wine grape berries Expert Systems with Applications 250 (123891). doi: 10.1016/j.eswa.2024.123891.

Boosting the performance of SOTA convolution-based networks with dimensionality reduction: An application on hyperspectral images of wine grape berries

Silva R., Gramaxo Freitas O., Melo-Pinto P., (2023) Boosting the performance of SOTA convolution-based networks with dimensionality reduction: An application on hyperspectral images of wine grape berries Intelligent Systems with Applications 19 (200252). doi: 10.1016/j.iswa.2023.200252.

Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning

Monteiro F., Bexiga V., Chaves P., Godinho J., Henriques D., Melo-Pinto P., Nunes T., Piedade F., Pimenta N., Sustelo L., Fernandes A.M., (2023) Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning Remote Sensing 15 (3952). ISSN: 20724292. doi: 10.3390/rs15163952.

Early yield prediction in different grapevine varieties using computer vision and machine learning

Palacios F., Diago M.P., Melo-Pinto P., Tardaguila J., (2023) Early yield prediction in different grapevine varieties using computer vision and machine learning Precision Agriculture 24 :407-435. ISSN: 15731618. doi: 10.1007/s11119-022-09950-y.

Multi-agent System for Multimodal Machine Learning Object Detection

Coelho E., Pimenta N., Peixoto H., Durães D., Melo-Pinto P., Alves V., Bandeira L., Machado J., Novais P., (2023) Multi-agent System for Multimodal Machine Learning Object Detection Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14001 LNAI :673-681. ISSN: 16113349. doi: 10.1007/978-3-031-40725-3_57.

t-SNE: A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters

Silva R., Melo-Pinto P., (2023) t-SNE: A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters Artificial Intelligence in Agriculture 7 :58-68. ISSN: 25897217. doi: 10.1016/j.aiia.2023.02.003.

Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data

Silva J., Pereira P., Machado R., Névoa R., Melo-Pinto P., Fernandes D., (2022) Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data Sensors 22 (2184). doi: 10.3390/s22062184.

Deep learning and computer vision for assessing the number of actual berries in commercial vineyards

Palacios F., Melo-Pinto P., Diago M.P., Tardaguila J., (2022) Deep learning and computer vision for assessing the number of actual berries in commercial vineyards Biosystems Engineering 218 :175-188. doi: 10.1016/j.biosystemseng.2022.04.015.

Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets

Couto P., Bento T., Bustince H., Melo-Pinto P., (2022) Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets Applied Sciences (Switzerland) 12 (4865). ISSN: 20763417. doi: 10.3390/app12104865.

A review of different dimensionality reduction methods for the prediction of sugar content from hyperspectral images of wine grape berries

Silva R., Melo-Pinto P., (2021) A review of different dimensionality reduction methods for the prediction of sugar content from hyperspectral images of wine grape berries Applied Soft Computing 113 (107889). doi: 10.1016/j.asoc.2021.107889.

Application of hyperspectral imaging and deep learning for robust prediction of sugar and ph levels in wine grape berries

Gomes V., Mendes-Ferreira A., Melo-Pinto P., (2021) Application of hyperspectral imaging and deep learning for robust prediction of sugar and ph levels in wine grape berries Sensors 21 (3459). doi: 10.3390/s21103459.

Determination of sugar, ph, and anthocyanin contents in port wine grape berries through hyperspectral imaging: An extensive comparison of linear and non-linear predictive methods

Gomes V., Rendall R., Reis M.S., Mendes-Ferreira A., Melo-Pinto P., (2021) Determination of sugar, ph, and anthocyanin contents in port wine grape berries through hyperspectral imaging: An extensive comparison of linear and non-linear predictive methods Applied Sciences (Switzerland) 11 (10319). ISSN: 20763417. doi: 10.3390/app112110319.

Object Detection Under Challenging Lighting Conditions Using High Dynamic Range Imagery

Mukherjee, R. ; Bessa, M. ; Melo-Pinto, P. ; Chalmers, A. (2021) Object Detection Under Challenging Lighting Conditions Using High Dynamic Range Imagery IEEE Acess 9 :71-83. doi: 10.1109/ACCESS.2021.3082293. (Impact factor, Quartile: 3.367, Q2).

Prediction of sugar content in port wine vintage grapes using machine learning and hyperspectral imaging

Gomes V., Reis M.S., Rovira-Más F., Mendes-Ferreira A., Melo-Pinto P., (2021) Prediction of sugar content in port wine vintage grapes using machine learning and hyperspectral imaging Processes 9 (1241). ISSN: 22279717. doi: 10.3390/pr9071241.

Towards robust Machine Learning models for grape ripeness assessment

Gomes V., Melo-Pinto P., (2021) Towards robust Machine Learning models for grape ripeness assessment JCSSE 2021 - 18th International Joint Conference on Computer Science and Software Engineering: Cybernetics for Human Beings (9493822). doi: 10.1109/JCSSE53117.2021.9493822.

Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks

Fernandes A.M., Utkin A.B., Eiras-Dias J., Cunha J., Silvestre J., Melo-Pinto P., (2019) Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks Computers and Electronics in Agriculture 163 (104855). doi: 10.1016/j.compag.2019.104855.

Assessment of grapevine variety discrimination using stem hyperspectral data and AdaBoost of random weight neural networks.

Fernandes, Armando; Utkin, Andrei; Eiras-Dias, José; Silvestre, José; Cunha, Jorge; Melo-Pinto, Pedro. (2018) Assessment of grapevine variety discrimination using stem hyperspectral data and AdaBoost of random weight neural networks. Applied Soft Computing 72 :140 - 155. doi: 10.1016/j.asoc.2018.07.059. (Impact factor, Quartile: 3.907, Q1).

Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries.

Silva, Rui; Gomes, Veronique; Mendes-Faia, Arlete; Melo-Pinto, Pedro (2018) Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries. Remote Sensing 10 (2). doi: 10.3390/rs10020312. (Impact factor, Quartile: 3.406, Q2).

Characterization of neural network generalization in the determination of pH and anthocyanin content of wine grape in new vintages and varieties.

Gomes, V, Fernandes, A, Martins-Lopes, P, Pereira, L, Faia, AM, Melo-Pinto, P (2017) Characterization of neural network generalization in the determination of pH and anthocyanin content of wine grape in new vintages and varieties. Food Chemisry 218 :40-46. ISSN: 0308-8146. doi: 10.1016/j.foodchem.2016.09.024. (Impact factor, Quartile: 4.529, Q1).

Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging.

Gomes, VM, Fernandes, AM, Faia, A, Melo-Pinto, P (2017) Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging. Computers And Electronics In Agriculture 140 :244-254. ISSN: 0168-1699. doi: 10.1016/j.compag.2017.06.009. (Impact factor, Quartile: 2.201, Q1).

Computed tomography and radiographic assessment of congruity between the ulnar trochlear notch and humeral trochlea in large breed dogs.

Alves-Pimenta, S, Ginja, MM, Fernandes, AM, Ferreira, AJ, Melo-Pinto, P, Colaço, B (2017) Computed tomography and radiographic assessment of congruity between the ulnar trochlear notch and humeral trochlea in large breed dogs. Veterinary And Comparative Orthopaedics And Traumatology 30 :14-Aug. ISSN: 0932-0814. doi: 10.3415/VCOT-16-03-0045. (Impact factor, Quartile: 0.917, Q2).

Radiographic assessment of humeroulnar congruity in amedium and a large breed of dog.

Coordinator / Pascual Campoy UTAD/CITAB Coordinator / Pedro Pinto
Start date / 2011/01 Funding / Plan Nacional de I + D + i 2008-2011. Ministerio de Ciencia y Innovación. Spain Reference / DPI2010-20751-C02-01

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New Information Representation and Aggregation Model using Fuzzy Sets Extensions. Applications.

Concluded

Coordinator / Humberto Bustince UTAD/CITAB Coordinator / Pedro Pinto
Start date / 2011/01 Funding / Plan Nacional de I + D + i 2010-2012. Ministerio de Ciencia y Innovación. Spain Reference / TIN2007-65981

See project details →