TY  - JOUR
AU  - Pereira, Matheus Costa
AU  - Azevedo, Tiago Martins de
AU  - Ribeiro, Caio Tertuliano
AU  - Freire, Ana Izabella
AU  - Francisco, Matheus Brendon
AU  - Pereira, João Luiz Junho
AU  - Paiva, Anderson Paulo de
PY  - 2025
DA  - 2025/11/15
TI  - Discriminating Planted Capsicum Spp. Varieties via Machine Learning and Multivariate Data Reduction
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 3
SP  - 166
EP  - 185
DO  - 10.62762/TMI.2025.385133
UR  - https://www.icck.org/article/abs/TMI.2025.385133
KW  - species prediction
KW  - pepper
KW  - machine learning
KW  - classification algorithms
KW  - factor analysis
AB  - The classification of Capsicum spp. varieties is often hindered by their morphological similarities, making accurate identification a challenging task. To address this issue, this study applies a hybrid computational approach that combines data dimensionality reduction techniques using Principal Component Analysis and Factor Analysis with various supervised Machine Learning algorithms. The dataset, which is unprecedented in the literature and was collected under controlled agricultural conditions, enables a robust evaluation of models including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest, Decision Tree, and Gradient Boosting. Model performance was assessed using Leave-One-Out and K-Fold cross-validation methods. Additionally, the SHapley Additive exPlanations method was applied to assess the importance of the features in species classification, providing greater interpretability that reinforces the relevance of morphological and agronomic descriptors in differentiating pepper varieties. The results show that all models achieved high performance metrics, including accuracy, F1-score, precision, and recall, consistently above 0.89, validating the effectiveness of the proposed approach. These findings highlight the potential of integrated Machine Learning frameworks for species classification in agriculture, contributing to practical applications and advancing intelligent analysis of biological data.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Pereira2025Discrimina,
  author = {Matheus Costa Pereira and Tiago Martins de Azevedo and Caio Tertuliano Ribeiro and Ana Izabella Freire and Matheus Brendon Francisco and João Luiz Junho Pereira and Anderson Paulo de Paiva},
  title = {Discriminating Planted Capsicum Spp. Varieties via Machine Learning and Multivariate Data Reduction},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {3},
  pages = {166-185},
  doi = {10.62762/TMI.2025.385133},
  url = {https://www.icck.org/article/abs/TMI.2025.385133},
  abstract = {The classification of Capsicum spp. varieties is often hindered by their morphological similarities, making accurate identification a challenging task. To address this issue, this study applies a hybrid computational approach that combines data dimensionality reduction techniques using Principal Component Analysis and Factor Analysis with various supervised Machine Learning algorithms. The dataset, which is unprecedented in the literature and was collected under controlled agricultural conditions, enables a robust evaluation of models including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest, Decision Tree, and Gradient Boosting. Model performance was assessed using Leave-One-Out and K-Fold cross-validation methods. Additionally, the SHapley Additive exPlanations method was applied to assess the importance of the features in species classification, providing greater interpretability that reinforces the relevance of morphological and agronomic descriptors in differentiating pepper varieties. The results show that all models achieved high performance metrics, including accuracy, F1-score, precision, and recall, consistently above 0.89, validating the effectiveness of the proposed approach. These findings highlight the potential of integrated Machine Learning frameworks for species classification in agriculture, contributing to practical applications and advancing intelligent analysis of biological data.},
  keywords = {species prediction, pepper, machine learning, classification algorithms, factor analysis},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}