Evaluation of the influence of multispectral imaging for object detection in pineapple crops

Abstract

Normally, most studies related to Object Detection focus only on RGB images. However, this research explores the feasibility of utilizing multispectral drone images, incorporating RGB channels with near-infrared, and red-edge channels, to perform Object Detection (OD) using drone images of pineapple crops. There are two main challenges when dealing with multi-spectral images. The first challenge is related to the alignment of the images when dealing with different cameras. Multispectral image alignment corrects for camera position and exposure time differences. We use SIFT and ORB for feature-based exposure matching after initial phase alignment. The second challenge is how to incorporate the extra channels into the RGB images, also known as channel fusion. Here, we studied two fusion techniques: early and late fusion. These techniques offer a comprehensive perspective on the potential of multispectral data to enhance object detection accuracy, although the anticipated leap in performance compared to conventional RGB imagery faced challenges. Finally, this research proves that using the correct alignment images process, considering the Vegetation Indexes, and also using the early fusion technique can assist in getting better results in order to improve the precision agriculture techniques.