Details
Name
Rui Costa MartinsCluster
Networked Intelligent SystemsRole
Senior ResearcherSince
15th November 2016
Nationality
PortugalCentre
Applied PhotonicsContacts
+351220402301
rui.c.martins@inesctec.pt
2022
Authors
Ferreira, MFS; Silva, NA; Guimarães, D; Martins, RC; Jorge, PAS;
Publication
U.Porto Journal of Engineering
Abstract
2022
Authors
Tosin, R; Martins, R; Pocas, I; Cunha, M;
Publication
BIOSYSTEMS ENGINEERING
Abstract
This paper focuses on predicting predawn leaf water potential through a self-learning artificial intelligence (SL-AI) algorithm, a novel spectral processing algorithm that is based on the search for covariance modes, providing a direct relationship between spectral information and plant constituents. The SL-AI algorithm was applied in a dataset containing 847 observations obtained with a handheld hyperspectral spectroradiometer (400–1010 nm), structured as: three grapevine cultivars (Touriga Nacional, Touriga Franca and Tinta Barroca), collected in three years (2014, 2015 and 2017), in two test sites in the renowned Douro Wine Region, northeast of Portugal. The ?pd SL-AI quantification was tested both in regressive (R2 = 0.97, MAPE = 18.30%) and classification (three classes; overall accuracy = 86.27%) approaches, where the radiation absorption spectrum zones of the chlorophylls, xanthophyll and water were identified along the vegetative growth cycle. The dataset was also tested with Artificial Neural Networks with Principal Component Analysis (ANN-PCA) and Partial Least Square (PLS), which presented worse performance when compared to SL-AI in the regressive (ANN-PCA - R2 = 0.85, MAPE = 43.64%; PLS - R2 = 0.94, MAPE = 28.76%) and classification (ANN-PCA - overall accuracy: 72.37%; PLS - overall accuracy: 73.79%) approaches. The ?pd modelled with SL-AI demonstrated, through hyperspectral reflectance, a cause-effect of the grapevine's hydric status with the absorbance of bands related to chlorophyll, xanthophylls and water. This cause-effect interaction could be explored to identify cultivars and cultural practices, hydric, heating and lighting stresses. © 2022 IAgrE
2022
Authors
Reis Pereira, M; Tosin, R; Martins, R; dos Santos, FN; Tavares, F; Cunha, M;
Publication
PLANTS-BASEL
Abstract
Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV-VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325-1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.
2022
Authors
Barroso, TG; Ribeiro, L; Gregorio, H; Monteiro Silva, F; dos Santos, FN; Martins, RC;
Publication
CHEMOSENSORS
Abstract
2021
Authors
Aguiar, AS; Magalhaes, SA; dos Santos, FN; Castro, L; Pinho, T; Valente, J; Martins, R; Boaventura Cunha, J;
Publication
AGRONOMY-BASEL
Abstract
Supervised Thesis
2021
Author
Miguel Fernandes Soares Ferreira
Institution
UP-FCUP
2020
Author
Miguel Fernandes Soares Ferreira
Institution
UP-FCUP
2020
Author
Ana Patrícia Ferreira Vicente da Silva
Institution
UP-FCUP
2019
Author
Miguel Fernandes Soares Ferreira
Institution
UP-FCUP
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