Coffee Tree Detection Using Convolutional Neural Network - Hasan, Ahmad Abdulsalam Naji (International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 2022)
Identifying plants is an important field in the environment because of their roles in the continuation of human existence. Finding a plant by using the traditional methods such as looking at its physical properties is a burdensome task. Thus, several computational-based methods have been introduced for detecting trees. In this study we constructed the coffee tree dataset due there is no publicly available coffee tree dataset for detection and classification of the coffee tree in orchard environments for what this tree has a role in health, industrial and agricultural fields, and raising the wheel of economic development. Many machine learning algorithms have been used to detect and classify trees which resulted in reliable results. In this study, we presented a deep learning-based approach, in particular a convolutional neural network, for coffee tree detection and classification. The current study focused on providing a dataset for the detection and classification of coffee trees and improving the efficiency of the algorithm used in the detection and classification model. This study achieved the best results, the proposed system achieved an accuracy of 0.97%.