Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery

Abstract

Weeds are among the significant factors that could harm crop yield by invading crops and smother pastures, and significantly decrease the quality of the harvested crops. Herbicides are widely used in agriculture to control weeds; however, excessive use of herbicides in agriculture can lead to environmental pollution as well as yield reduction. Accurate mapping of crops/weeds is essential to determine weeds’ location and locally treat those areas. Increasing demand for flexible, accurate and lower cost precision agriculture technology has resulted in advancements in UAS-based remote sensing data collection and methods. Deep learning methods have been successfully employed for UAS data processing and mapping tasks in different domains. This research investigate, compares and evaluates the performance of deep learning methods for crop/weed discrimination on two open-source and published benchmark datasets captured by different UASs (field robot and UAV) and labeled by experts. We specifically investigate the following architectures: 1) U-Net Model 2) SegNet 3) FCN (FCN-32s, FCN-16s, FCN-8s) 4) DepLabV3+. The deep learning models were fine-tuned to classify the UAS datasets into three classes (background, crops, and weeds). The classification accuracy achieved by U-Net is 77.9% higher than 62.6% of SegNet, 68.4% of FCN-32s, 77.2% of FCN-16s, and slightly lower than 81.1% of FCN-8s, and 84.3% of DepLab v3+. Experimental results showed that the ResNet-18 based segmentation model such as DepLab v3+ could precisely extract weeds compared to other classifiers.