Wavelet-based crop detection and classification

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.