Development of an Extreme Machine Learning-Based Computational Application for the Detection of Armillaria in Cherry Trees

This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of machine learning (ML) models, using the optimal variant among different Extreme Learning Machine (ELM) models. This tool represents a concrete contribution to the use of artificial intelligence in smart agriculture, enabling more efficient and accessible management of cherry tree crops. The overall goal is to evaluate machine learning-based strategies that enable efficient and low-computational-cost detection of the disease, facilitating its implementation on devices with limited resources. The ERICA database is used by following a proper methodology in order to learning and validation stages are completely independent. Preprocessing includes renaming, cropping, scaling, grayscale conversion, vectorization, and normalization. Subsequently, the impact of reducing image resolution is studied, identifying that a size of 63 × 23 pixels maintains a good balance between visual detail and computational efficiency. Six ELM variants are trained: standard, regularized (R-ELM), class-weighted (W1-ELM and W2-ELM), and multilayer (ML2-ELM and ML3-ELM), and classical machine learning approaches are optimized and compared with classical ML approaches. The results indicate that W1-ELM achieves the best performance among tested variants, reaching an accuracy of 0.77 and a geometric mean of 0.45 with a training time in order of seconds.