Details
Name
Mário CunhaCluster
Industrial and Systems EngineeringRole
Senior ResearcherSince
01st February 2018
Nationality
PortugalCentre
Robotics in Industry and Intelligent SystemsContacts
+351220413317
mario.cunha@inesctec.pt
2022
Authors
Monteiro, AT; Alves, P; Carvalho Santos, C; Lucas, R; Cunha, M; da Costa, EM; Fava, F;
Publication
DIVERSITY-BASEL
Abstract
2022
Authors
Martins, RC; Barroso, TG; Jorge, P; Cunha, M; Santos, F;
Publication
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Abstract
2022
Authors
Moreira, G; Magalhaes, SA; Pinho, T; dos Santos, FN; Cunha, M;
Publication
AGRONOMY-BASEL
Abstract
2022
Authors
Guo, YH; Chen, SZ; Li, XX; Cunha, M; Jayavelu, S; Cammarano, D; Fu, YS;
Publication
REMOTE SENSING
Abstract
Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R-2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R-2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.
2022
Authors
Afonso, S; Dias, MI; Ferreira, ICFR; Arrobas, M; Cunha, M; Barros, L; Rodrigues, MA;
Publication
HORTICULTURAE
Abstract
Supervised Thesis
2021
Author
Dario Ho de Almeida Santos
Institution
UP-FCUP
2020
Author
Mafalda Alexandra Reis Pereira
Institution
UP-FCUP
2020
Author
Sérgio Duarte Moreira Fernandes
Institution
UP-FCUP
2020
Author
Sandra Cristina Pereira Afonso
Institution
UP-FCUP
2020
Author
Renan Tosin
Institution
UP-FCUP
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