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

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%.