Classification of Oil Palm Trees Using Quantum Convolutional Neural Network (QCNN)

Amirullah, Desi
Gultom, ipantri Mashur

Proceeding Applied Business and Engineering Conference
Vol. 12 (2024): 12th Applied Business and Engineering Conference

The oil palm trees (Elaeis guineensis) is an important commodity in the plantation industry, and the classificationof its varieties is crucial for enhancing productivity and harvesting efficiency. This study aims to apply QuantumConvolutional Neural Network (QCNN) as a method for classifying oil palm trees. QCNN integrates quantum computingprinciples into the architecture of convolutional neural networks, allowing for more efficient and accurate data processing.The data used in this research includes oil palm plantations located in the coastal areas of Bengkalis Island. Data acquisitionwas performed using a DJI Phantom 4 Pro drone, capturing vertical images from above. The classification process utilizedkey features extracted from the images using the QCNN algorithm. The results of the experiments show that QCNNachieved a training accuracy of 94.7% and a testing accuracy of 92.5%. Thus, this research makes a significant contributionto the development of oil palm tree classification technology and opens new opportunities for the application of quantumalgorithms in agriculture. These findings are expected to assist farmers and researchers in identifying and managing oilpalm tree varieties more effectively, thereby supporting the sustainability and productivity of the oil palm industry as awhole.