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

Authors

  • Bandar Al-Rami Arab open University, KSA
  • Khattab M. Ali Alheeti College of Computer Sciences and Information Technology, Computer Networking Systems Department
  • Waleed M. Aldosari Prince Sattam Bin Abdulaziz University, College Computer Engineering and Science, Dept. Computer Engineering, Al kharj, 11942, Saudi Arab
  • Saeed Matar Alshahrani Saudi Electronic University
  • Shahad Mahdi Al-Abrez College of Computer Sciences and Information Technology, Computer Networking Systems Department, University of Anbar

DOI:

https://doi.org/10.3991/ijim.v16i09.30037

Keywords:

Crop classification, drone, transfer learning, smart farming

Abstract


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%.

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Published

2022-05-10

How to Cite

Al-Rami, B. ., Alheeti, K. M. A. ., Aldosari, W. M., Alshahrani, S. M. ., & Al-Abrez, S. M. . (2022). A New Classification Method for Drone-Based Crops in Smart Farming. International Journal of Interactive Mobile Technologies (iJIM), 16(09), pp. 164–174. https://doi.org/10.3991/ijim.v16i09.30037

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Papers