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

Sajjad Saeed

doi:10.52584/qrj.1901.01

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.

Highlights

Crop classification is important to monitor agriculture farming operations that are used to increase crop yield, variable rate fertility, and helps farmers to understand the effects of climate change on crops
We calculated the Root Mean Squared Error (RMSE) at each epoch of the training dataset to see the progress of the training of the model
Gradient descent (GD) and Least square estimate (LSE) are two other rules that affect the computational time of adaptive neuro fuzzy inference system (ANFIS) [23]

Summary

Introduction

Crop classification is important to monitor agriculture farming operations that are used to increase crop yield, variable rate fertility, and helps farmers to understand the effects of climate change on crops. Accuracy in crop classification systems is critical to precisely address the stress in the plants triggered by different biotic and abiotic factors that helps in taking appropriate decisions at right time [1]. Most of the countries are using different precision agriculture techniques to enhance the economy by producing high quality crop yields. Crop classification may produce incorrect labels as many crops exhibit the same color, texture, and other visual characteristics at early stage leading to produce similar spectral signatures for different crops [2]. Satellitebased remote sensing techniques are in use since decades for large area estimations quite efficiently, but are not so effective for small scale farming with complex crop patterns. The provision is achievable through UAV (Unmanned Aerial Vehicle)

Objectives

Methods

Results

Conclusion