Philippine Journal of Science
150 (3): 1039-1050, June 2021
ISSN 0031 – 7683
Date Received: 17 Dec 2020

Integrated Weed Estimation and Pest Damage
Detection in Solanum melongena Plantation
via Aerial Vision-based Proximal Sensing

Anton Louise P. de Ocampo¹* and Elmer P. Dadios²

¹Electronics, Instrumentation, and Mechatronics Engineering Department
Batangas State University, Batangas City 4217 Philippines
²Manufacturing Engineering and Management Program
De La Salle University, Manila 1003 Philippines

*Corresponding author: antonlouise.deocampo@g.batstate-u.edu.ph

ABSTRACT

The Philippine government’s effort to transcend agriculture as an industry requires precision agriculture. Remote- and proximal-sensing technologies help to identify what is needed, when, and where it is needed in the farm. This paper proposes the use of vision-based indicators captured using a low-altitude unmanned aerial vehicle (UAV) to estimate weed and pest damages. Coverage path planning is employed for automated data acquisition via UAV. The gathered data are processed in a ground workstation employing the proposed methods in estimating vegetation fraction, weed presence, and pest damages. The data processing includes techniques on sub-image level classification using a hybrid ResNet-SVM model and normalized triangular greenness index. Sub-image level classification for isolating crops from the rest of the image achieved an F1-score of 97.73% while pest damage detection was performed with an average accuracy of 86.37%. Also, the weed estimate achieved a true error of 5.72%.