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

International Journal of Interactive Mobile Technologies

article

Bandar Al-Rami¹  Khattab M. Ali Alheeti²  Waleed M. Aldosari³  Saeed Matar Alshahrani⁴  Shahad Mahdi Al-Abrez⁵ 

[1] Arab open University;College of Computer Sciences and Information Technology, Computer Networking Systems Department;Prince Sattam Bin Abdulaziz University, College Computer Engineering and Science, Dept. Computer Engineering;Saudi Electronic University;College of Computer Sciences and Information Technology, Computer Networking Systems Department, University of Anbar

关键词: Crop classification;    drone;    transfer learning;    smart farming;   

DOI  :  10.3991/ijim.v16i09.30037

学科分类：社会科学、人文和艺术（综合）

来源: International Association of Online Engineering

【 摘 要 】

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%.

【 授权许可】

CC BY