Abstract:
Crop row detection is a vital task in precision agriculture. Earlier works for solving this task follow traditional computer vision based methodologies. However, in recen...Show MoreMetadata
Abstract:
Crop row detection is a vital task in precision agriculture. Earlier works for solving this task follow traditional computer vision based methodologies. However, in recent years deep learning based approaches are being adopted for solving this task. Among various deep learning methodologies, semantic segmentation has found to be most successful for obtaining meaningful representation of images in a plethora of domains, such as, medical image analysis and autonomous driving. Scene parsing is a subcategory of semantic segmentation where all objects of interest in a scene are color coded as a way to simultaneously classify and localize their presence. In this way, scene parsing technique is a very good fit for solving crop row detection task; However, no existing research has yet ventured this direction. In this work we investigate the performance of five latest semantic segmentation methodologies on real-life crop row datasets for solving the crop row detection task. Our experimental results validate that most of the semantic segmentation methods provide substantially good results for solving the crop row detection task; Importantly, LinkNet architecture provides the best results among the competitors. We also discuss various reallife challenges for solving crop row detection in real-life scenarios.
Date of Conference: 17-20 December 2022
Date Added to IEEE Xplore: 26 January 2023
ISBN Information:
Funding Agency:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Semantic Segmentation ,
- Semantic Approach ,
- Crop Row ,
- Semantic Segmentation Approaches ,
- Crop Row Detection ,
- Deep Learning ,
- Computer Vision ,
- Real-life Scenarios ,
- Precision Agriculture ,
- Medical Image Analysis ,
- Semantic Segmentation Methods ,
- Scene Parsing ,
- Neural Network ,
- Training Set ,
- Convolutional Neural Network ,
- Convolutional Layers ,
- Spatial Information ,
- Feature Maps ,
- Crop Growth ,
- Object Detection ,
- Crop Growth Stages ,
- Feature Pyramid Network ,
- Pyramid Pooling Module ,
- Hough Transform ,
- Semantic Segmentation Models ,
- Early Growth Stages ,
- Feature Pyramid ,
- Crop Fields ,
- Crop Stages ,
- Segmentation Map
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Semantic Segmentation ,
- Semantic Approach ,
- Crop Row ,
- Semantic Segmentation Approaches ,
- Crop Row Detection ,
- Deep Learning ,
- Computer Vision ,
- Real-life Scenarios ,
- Precision Agriculture ,
- Medical Image Analysis ,
- Semantic Segmentation Methods ,
- Scene Parsing ,
- Neural Network ,
- Training Set ,
- Convolutional Neural Network ,
- Convolutional Layers ,
- Spatial Information ,
- Feature Maps ,
- Crop Growth ,
- Object Detection ,
- Crop Growth Stages ,
- Feature Pyramid Network ,
- Pyramid Pooling Module ,
- Hough Transform ,
- Semantic Segmentation Models ,
- Early Growth Stages ,
- Feature Pyramid ,
- Crop Fields ,
- Crop Stages ,
- Segmentation Map
- Author Keywords