Analysis of Spectral Bands and Spatial Resolutions for Weed Classification Via Deep Convolutional Neural Network

Abstract

Weed detection and classification are one of the important and crucial steps for area-specific weed control. This reduces the overall cost and the negative impact of using unnecessary herbicides on human health and crops. As the spectral similarity between weeds and crops is high, patch-based classification approaches are adopted in this letter. Convolutional neural network (CNN) and histogram of oriented gradients (HoG) methods are evaluated and compared. With the advancement in the remote sensing technologies, a large number of sensors are available which provide different number of bands with different spatial resolutions. The effect of these variations on weed identification is investigated. Experimental results show that CNN method extracts more discriminative and powerful features that lead to an accurate classification of different weeds compared to the HoG method. Analysis of the two important parameters provides guidance in choosing correct patch size, spatial resolution, and the number of bands to use when CNN is applied for accurate and efficient weed classification.