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Sobre

Sobre

Mestre em Engenharia Agronómica pela Faculdade de Ciências da Universidade do Porto (FCUP) no âmbito da temática "Phenological forecasting models and pollen quality of Vitis vinifera L.", Mafalda Reis Pereira é atualmente estudante no Programa Doutoral em Ciências Agrárias da FCUP. Trabalha atualmente na área da "Deteção e identificação precoce de doenças das plantas provocadas por bactérias com base na detecção proximal numa óptica de agricultura de precisão", tendo recebido uma bolsas de investigação para doutoramento da Fundação para a Ciência e a Tecnologia - FCT - com a referência SFRH/BD/146564/2019. Integrada no Centro de Robótica Industrial e Sistemas Inteligentes (CRIIS), é também um Elemento do Grupo de Aconselhamento para a Diversidade e Inclusão do INESCTEC e Elemento da Rede de Embaixadores Interculturais do INESCTEC. Interesses: Agricultura de Precisão | Patologia de Plantas | Microbiologia | Modelação | Ciência de Dados | Machine Learning | Espectroscopia

Tópicos
de interesse
Detalhes

Detalhes

Publicações

2022

Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae

Autores
Reis Pereira, M; Tosin, R; Martins, R; dos Santos, FN; Tavares, F; Cunha, M;

Publicação
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.