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

Author(s)
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,Pakistan
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.
Publication Details
Page(s) 1-9
DOI 10.52584/QRJ.1901.01
Published Journal: Quaid-e-Awam University Research Journal of Engineering, Science and Technology, Volume: 19, Issue: 1, Year: 2021

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Keywords
NDVI, fuzzy logic, ANFIS, UAV, crop classification
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