DEEP LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION FOR AGRICULTURE APPLICATIONS
- 1Geomatics Program, Department of Built Environment, North Carolina A&T State University, USA
- 2Applied Science and Technology Ph.D. Program, Department of Built Environment, North Carolina A&T State University, USA
Keywords: Precision Agriculture, Remote Sensing, U-Net, FCN-8s, Deep Learning, Image Segmentation
Abstract. This research examines the ability of deep learning methods for remote sensing image classification for agriculture applications. U-net and convolutional neural networks are fine-tuned, utilized and tested for crop/weed classification. The dataset for this study includes 60 top-down images of an organic carrots field, which was collected by an autonomous vehicle and labeled by experts. FCN-8s model achieved 75.1% accuracy on detecting weeds compared to 66.72% of U-net using 60 training images. However, the U-net model performed better on detecting crops which is 60.48% compared to 47.86% of FCN-8s.