A New Classification Method for Drone-Based Crops in Smart Farming

A New Classification Method for Drone-Based Crops in Smart Farming

Crop classification, drone, transfer learning, smart farming

During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for increasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and remote sensing usage for many different applications such as crop classification, crop health monitoring, and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolutional Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93%.

Al-Rami, Bandar

Alheeti, Khattab M. Ali

Aldosari, Waleed M.

Alshahrani, Saeed Matar

Al-Abrez, Shahad Mahdi

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 09 (2022); pp. 164-174

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-05-10

Copyright (c) 2022 Haider Th.Salim Alrikabi; Bandar Al-Rami, Khattab M. Ali Alheeti, Waleed M. Aldosari, Saeed Matar Alshahrani, Shahad Mahdi Al-Abrez

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article