weed classification based on haar wavelet transform via k-nearest neighbor (k-nn) for real-time automatic sprayer control system
Weeds cause harm to crops by competing for water, light, nutrients and space, reducing crop yields and inhibiting the efficiency of machinery. Although there is a large volume of work on this topic, previous studies have lacked accuracy and efficiency. In this paper an algorithm has been developed and analyzed for real-time specific weeds discrimination that is employed Haar Wavelet Transform (HWT). There are three stages of this paper, segmentation stage, feature extraction stage, and classification stage. Based on the proposed algorithm, 200 highest and informative coefficients are extracted at feature extraction stage, then at the classification stage, a well known classifier k-NN (k-Nearest Neighbor, for which k = 2) has been applied. The proposed method was tested on the database of 200 samples of each category. Furthermore, the result of our proposed method improves the performance by 7--10\% as compared with some of the existing techniques that used histogram maxima with threshold, and angular cross sectional intensities. In conclusion, the proposed technique is more accurate at weed leaf classification than the existing techniques when images are captured by a CCD camera. The overall accuracy and efficiency utilizing the proposed method using haar wavelet transform is 94\% and 40 ms respectively.
weed hwt machine vision knn herbicides