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 Tasks

Sustainability in agri-food and forestry ecosystems

Innovative technologies and processes

Publications

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.

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