Premise: Black Sigatoka is one of the most severe banana (Musa spp.) diseases worldwide, but no methods for the rapid early detection of this disease have been reported. This paper assesses the use of hyperspectral images for the development of a partial‐least‐squares penalized‐logistic‐regression (PLS–PLR) model and a hyperspectral biplot (HS biplot) as a visual tool for detecting the early stages of black Sigatoka disease.Methods: Young (three‐month‐old) banana plants were inoculated with a conidia suspension of the black Sigatoka fungus (Pseudocercospora fijiensis). Selected infected and control plants were evaluated using a hyperspectral imaging system at wavelengths in the range of 386–1019 nm. PLS–PLR models were run on the hyperspectral data set. The prediction power was assessed using leave‐one‐out cross‐validation as well as external validation.Results: The PLS–PLR model was able to predict the presence of the disease with a 98% accuracy. The wavelengths with the highest contribution to the classification ranged from 577 to 651 nm and from 700 to 1019 nm.Discussion: PLS–PLR and HS biplot effectively estimated the presence of black Sigatoka disease at the early stages and can be used to graphically represent the relationship between groups of leaves and both visible and near‐infrared wavelengths.

This study explores the use of generative artificial intelligence to enhance teaching and learning experience, focusing on strengthening and consolidating cognitive schemas. Research reveals that schemas can profoundly influence the improvement of the learning experience and promote the assimilation of new types of information and retention in students' memory. To improve the teaching and learning experience, the advantages, obstacles, and potential future trajectories of utilizing these technologies were examined by conducting a thorough literature review and analyzing relevant studies. Findings indicate that generative artificial intelligence has the potential to personalize learning, diversify educational content, and improve teaching efficiency and scalability. However, it also poses challenges related to content quality, data privacy, and equity in access to personalized learning. Future research should focus on the effectiveness of educational tools based on generative AI that promote equity and inclusion, ethical approaches, and interdisciplinary collaboration. Overall, this study provides a solid foundation for understanding and harnessing the potential of generative artificial intelligence in enhancing cognitive schemas, thereby promoting more effective, inclusive, and personalized education.

Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus (Pseudocercospora fijiensis) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square–discriminant analysis (NPLS-DA), and partial least square–penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models’ evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.