A Cascaded Deep Learning Approach for Detection and Localization of Crop-Weeds in RGB Images

Abstract

Weeds compete with crops in the fields, thus lowering crop yield with losses of up to 80%. The efficient use of chemical herbicides is desired to reduce the harmful effects on the environment, which requires the location of the weeds to be known. In this paper, we present a deep learning approach capable of detecting and localizing weeds in RGB images, trained using the publicly available Open Sprayer dataset. The adopted methodology consists of a classification step using a pre-trained 2D convolution neural network and a Random Forest classifier, which is used to predict the presence of weeds in an RGB image. If presence is predicted, then an attempt to localize them has been done by cascading a segmentation step using a U-Net architecture. The proposed architecture can classify the presence of weeds in an image with an accuracy of 91.19% and predict the location of weeds in the image by generating binary masks with a mean Dice score of 0.879 on the publicly available Open Sprayer dataset. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.