Identifying Rust Infection and Estimating Severity on Coffee Leaves Using Vision-Based ANN-KNN- Thresholding Methods
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
(2023), pp. 1-6
Pocholo James M. Loresco
a
,
Raymond Joseph Meimban
b
,
Alpanorwen Aseo
c
,
Rogelio Aniez
d
,
Nioro Furiscal
c
,
Earl Jan Jugueta
c
a Electronics Engineering, FEU Institute of Technology, Manila, Philippines
b School of Electronics Engineering, National University, Manila, Philippines
c School of Civil Engineering, National University, Manila, Philippines
d School of Electrical Engineering, National University, Manila, Philippines
Abstract: 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.