Identifying Rust Infection and Estimating Severity on Coffee Leaves Using Vision-Based ANN-KNN- Thresholding Methods

The coffee rust disease threatens coffee production in the Philippines with widespread defoliation and reduced yield. Identifying rust infection and its severity is critical for implementing effective mitigation strategies. As an alternative to recent methods that rely on deep learning approaches, our vision-based approach utilizes Artificial Neural Networks, K-Nearest Neighbors, and Thresholding methods to identify rust infection on coffee leaves and estimate severity, providing a computationally lightweight alternative for agricultural disease management. Twenty-four (24) color and texture features of a collected dataset of coffee leaf images were extracted as inputs for an ANN classifier. The percentage of damage on coffee leaves was determined by comparing the damaged pixels to the total area of the leaf using KNN and thresholding segmentation techniques. Through the use of confusion matrix and RMSE, the decision support system has demonstrated promising results in identifying coffee leaf health and estimating severity of coffee rust infection.

I. Introduction

In the modern world, new technologies are developing swiftly, and it makes sense that technology particular to the coffee industry has evolved. This includes modifications to everything from how coffee is roasted to how it is brewed. Moreover, as the coffee industry has flourished, technology has gained prominence across the supply chain. Within a few decades, African and Brazilian coffees, as well as Filipino coffee, were regarded as major competitors on the international market [1]. However, the Philippine sector suffered a fall in 1889 as a result of the introduction of coffee rust, a fungal disease that kills coffee plants. Huge swaths of productive regions were negatively damaged by the infestation as it swiftly expanded throughout the country [2].