Crop-Weed Discrimination Via Wavelet-Based Texture Analysis

Abstract

The worldwide problem of environmental pollution caused by excessive use of herbicides and the increasing cost of chemicals necessitates
finding alternative methods for crop protection. In order to reduce the quantities of herbicides applied to fields, we propose to exploit the advantages
of image processing to automatically detect and localize the crops and to remove all other undesired plants growing within rows and between
two crops. Wavelet transformation of digital images discriminates several spatial orientations and it is very effective in analyzing the information
content of images. In this study wavelet Analysis is used to extract appropriate features for classification. The mask is calculated for each sub-band
of wavelet transform. Then, energy, entropy, contrast, homogeneity and inertia features are extracted from each sub-band. Finally, these features are
feed into a multi-layer perceptron neural network to classify Corn and Weeds from each other commonly found in corn farms. Results showed that
this technique was able to discriminate corn plants with a very significant accuracy in comparing with the state-of-the art techniques.