Fuzzy-Based Multi-Crop Classification Using High Resolution UAV Imagery

  • Sajjad Saeed Department of Computer Science, University of Agriculture, Faisalabad, Pakistan
  • Muhammad Ahsan Latif Department of Computer Science, University of Agriculture, Faisalabad, Pakistan
  • Muhammad Awais Rajput Department of Artificial Intelligence, QUEST, Nawabshah
DOI: https://doi.org/10.52584/QRJ.1901.01
Keywords: ANFIS, Crop classification, Fuzzy logic, NDVI, UAV

Abstract

Accurate information regarding crop classification is important to estimation crop yield. It is used to depict the
relationship between exponentially growing world population and food demand. The purpose of this research is to recognize multiple crops in a single UAV-based image. The task itself is chaotic as every crop exhibits similar hue, color and other plant characteristics. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to classify 17 different crops based on their high spatial and temporal signatures of normalized difference vegetation index (NDVI) values acquired through multispectral sensor onboard a quadrotor. The multispectral images were classified into two classes (soil and crop) and NDVI signatures for each crop were extracted from images. Detailed dataset was prepared as a timeline through sampling, covering almost all phenological phases of the crops. The NDVI dataset was passed through ANFIS to classify NDVI vectors. ANFIS had only one output variable: the crop type that was formulated from 8 input variables. ANFIS used 2 membership functions for one input variable and formulated 256 fuzzy rules for the classification. The results show a high level of classification accuracy.

Published
2021-06-30
How to Cite
Saeed, S., Latif, M. A., & Rajput, M. A. (2021). Fuzzy-Based Multi-Crop Classification Using High Resolution UAV Imagery. Quaid-E-Awam University Research Journal of Engineering, Science & Technology, Nawabshah., 19(1), 1-8. https://doi.org/10.52584/QRJ.1901.01
Issue
Vol 19 No 1 (2021): Vol. 19, No. 1, JAN-JUN 2021
Section
Articles

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