Frontiers

Identification of tobacco leaf diseases using hyperspectral imaging and machine learning with SHAP interpretability analysis

Workflow of the hyperspectral tobacco disease classification framework. The pipeline includes data acquisition and preprocessing, SG-based smoothing and SPA wavelength selection, model training using ...
Frontiers

Early detection of sesame leaf disease using hyperspectral imaging and machine learning: a patch-based spatial-spectral integration approach

1 Department of Upland Crop Research and Development, National Institute of Crop Science, Rural Development Administration, Miryang, Gyeongsangnam-do, Republic of Korea 2 Department of Applied ...
Scientific Research Publishing

Comparative Study of Machine Learning Techniques for Early Detection of Heart Diseases ()

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for millions of deaths each year according to the World Health Organization (WHO). Early detection of these diseases ...
SciELO

LEAF NITROGEN CLASSIFICATION OF COMMON BEAN (PHASEOLUS VULGARIS L.) USING DEEP LEARNING MODELS AND IMAGES

Beans are a legume that is widely grown and consumed globally, being the staple food for humans in developing countries. Nitrogen (N) is the most limiting nutrient for yield and foliar analysis is ...
IEEE

Exploring Deep Learning and Explainable AI for Precise Rice Leaf Disease Detection and Classification

Abstract: This study presents an innovative approach to detect and classify four types of rice leaf diseases, including bacterial leaf blight, brown spot, tungro, and blast, as well as healthy leaves, ...