{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/130233"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/130233","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Millimeter-wave radar dataset for multi-modal fusion and keypoint detection in under-canopy soybean and corn row navigation","abstract":"This thesis addresses the persistent challenge of autonomous navigation beneath dense crop canopies, where common sensors RGB cameras, LiDAR, and GNSS often fail due to occlusion and degraded visibility. We a novel under-canopy dataset, collected over two growing seasons in corn and soybean fields, that integrates 77 GHz millimeter-wave (mmWave) radar with RGB-D stereo imagery and inertial measurements under a range of environmental conditions (low light, dust, and foliage cover). To our knowledge, this is the first multi-modal dataset of its kind in agricultural settings. Building on this resource, we investigate an end-to-end deep learning framework for radar-based keypoint prediction that leverages unprocessed range–azimuth radar data. The model employs knowledge distillation from a vision-based teacher network and fuses radar and camera features within an encoder–decoder architecture supervised by soft heatmap targets. Experimental evaluation highlights both the potential and limitations of using raw mmWave radar for under-canopy navigation, particularly with respect to computational demands. Our contributions include the new multimodal dataset, an agriculture-tailored fusion architecture, and a training methodology for radar-vision integration. These findings suggest that the incorporation of radar can enhance perception robustness in precision farming, paving the way for future work on real-time closed-loop control, cross-crop generalization, and multitask radar vision applications for plant monitoring and yield prediction.","abstract_html":"This thesis addresses the persistent challenge of autonomous navigation beneath dense crop canopies, where common sensors RGB cameras, LiDAR, and GNSS often fail due to occlusion and degraded visibility. We a novel under-canopy dataset, collected over two growing seasons in corn and soybean fields, that integrates 77 GHz millimeter-wave (mmWave) radar with RGB-D stereo imagery and inertial measurements under a range of environmental conditions (low light, dust, and foliage cover). To our knowledge, this is the first multi-modal dataset of its kind in agricultural settings. Building on this resource, we investigate an end-to-end deep learning framework for radar-based keypoint prediction that leverages unprocessed range–azimuth radar data. The model employs knowledge distillation from a vision-based teacher network and fuses radar and camera features within an encoder–decoder architecture supervised by soft heatmap targets. Experimental evaluation highlights both the potential and limitations of using raw mmWave radar for under-canopy navigation, particularly with respect to computational demands. Our contributions include the new multimodal dataset, an agriculture-tailored fusion architecture, and a training methodology for radar-vision integration. These findings suggest that the incorporation of radar can enhance perception robustness in precision farming, paving the way for future work on real-time closed-loop control, cross-crop generalization, and multitask radar vision applications for plant monitoring and yield prediction.","abstract_has_math":false,"creators":["Mihigo, Aganze"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Amato, Nancy","Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-25","date_published":"2025-07-25","updated_at":"2026-07-22T22:25:06Z","subjects":["Robotics"],"languages":["eng"],"rights":["Copyright 2025 Aganze Mihigo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/130233","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Amato, Nancy","Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Mihigo, Aganze"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-25","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation","Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robotics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Aganze Mihigo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/130233"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis addresses the persistent challenge of autonomous navigation beneath dense crop canopies, where common sensors RGB cameras, LiDAR, and GNSS often fail due to occlusion and degraded visibility. We a novel under-canopy dataset, collected over two growing seasons in corn and soybean fields, that integrates 77 GHz millimeter-wave (mmWave) radar with RGB-D stereo imagery and inertial measurements under a range of environmental conditions (low light, dust, and foliage cover). To our knowledge, this is the first multi-modal dataset of its kind in agricultural settings. Building on this resource, we investigate an end-to-end deep learning framework for radar-based keypoint prediction that leverages unprocessed range–azimuth radar data. The model employs knowledge distillation from a vision-based teacher network and fuses radar and camera features within an encoder–decoder architecture supervised by soft heatmap targets. Experimental evaluation highlights both the potential and limitations of using raw mmWave radar for under-canopy navigation, particularly with respect to computational demands. Our contributions include the new multimodal dataset, an agriculture-tailored fusion architecture, and a training methodology for radar-vision integration. These findings suggest that the incorporation of radar can enhance perception robustness in precision farming, paving the way for future work on real-time closed-loop control, cross-crop generalization, and multitask radar vision applications for plant monitoring and yield prediction."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Millimeter-wave radar dataset for multi-modal fusion and keypoint detection in under-canopy soybean and corn row navigation"]}]}],"canonical_facts":{"dc:contributor":["Amato, Nancy","Chowdhary, Girish"],"dc:creator":["Mihigo, Aganze"],"dc:date":["2025-07-25","2025-08"],"dc:description":["This thesis addresses the persistent challenge of autonomous navigation beneath dense crop canopies, where common sensors RGB cameras, LiDAR, and GNSS often fail due to occlusion and degraded visibility. We a novel under-canopy dataset, collected over two growing seasons in corn and soybean fields, that integrates 77 GHz millimeter-wave (mmWave) radar with RGB-D stereo imagery and inertial measurements under a range of environmental conditions (low light, dust, and foliage cover). To our knowledge, this is the first multi-modal dataset of its kind in agricultural settings. Building on this resource, we investigate an end-to-end deep learning framework for radar-based keypoint prediction that leverages unprocessed range–azimuth radar data. The model employs knowledge distillation from a vision-based teacher network and fuses radar and camera features within an encoder–decoder architecture supervised by soft heatmap targets. Experimental evaluation highlights both the potential and limitations of using raw mmWave radar for under-canopy navigation, particularly with respect to computational demands. Our contributions include the new multimodal dataset, an agriculture-tailored fusion architecture, and a training methodology for radar-vision integration. These findings suggest that the incorporation of radar can enhance perception robustness in precision farming, paving the way for future work on real-time closed-loop control, cross-crop generalization, and multitask radar vision applications for plant monitoring and yield prediction."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/130233"],"dc:language":["eng"],"dc:rights":["Copyright 2025 Aganze Mihigo"],"dc:subject":["Robotics"],"dc:title":["Millimeter-wave radar dataset for multi-modal fusion and keypoint detection in under-canopy soybean and corn row navigation"],"dc:type":["Thesis / Dissertation","Text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}