DEEP LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION FOR AGRICULTURE APPLICATIONS
Leila Hashemi-Beni, Asmamaw Gebrehiwot
2020The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences30 citationsDOIOpen Access PDF
Abstract
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.
Topics & Concepts
Deep learningConvolutional neural networkArtificial intelligenceField (mathematics)Computer scienceAgricultureContextual image classificationPrecision agricultureCropOrganic farmingImage (mathematics)Pattern recognition (psychology)Remote sensingAgricultural engineeringMachine learningGeographyMathematicsForestryEngineeringPure mathematicsArchaeologySmart Agriculture and AIRemote Sensing in AgricultureRemote-Sensing Image Classification