{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124344"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124344","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An exploratory journey of representation learning’s enhancement, adaptation and related intelligent methods","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Wu, Jing"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hovakimyan, Naira","Salapaka, Srinivasa","Martin, Nicolas Federico","Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Representation Learning","Machine Learning","Intelligent Agriculture"],"languages":["en","eng"],"rights":["Copyright 2024 Jing Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124344","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira","Salapaka, Srinivasa","Martin, Nicolas Federico","Wang, Yuxiong"]},{"key":"dc:creator","label":"Author","values":["Wu, Jing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-24"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Representation Learning","Machine Learning","Intelligent Agriculture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Jing Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124344"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Jing Wu, accepted the attached license on 2024-04-23 at 14:04.","The student, Jing Wu, submitted this Dissertation for approval on 2024-04-23 at 14:13.","This Dissertation was approved for publication on 2024-04-24 at 15:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20496 on 2024-09-16 at 00:35:29","Representation learning models employing Siamese structures have consistently demonstrated exceptional performance across various fields, including deep learning, computer vision, and natural language processing. Furthermore, the applicability of representation learning has broadened to encompass wider domains such as agriculture, remote sensing, and earth observation, which are significantly challenged by data scarcity. This dissertation aims to enhance the quality and adaptability of learned representations across these diverse application domains. Meanwhile, we have also expanded the scope of our research to a broader area of intelligent agricultural systems. Initially, we delve into contrastive representation learning within the general computer vision domain and introduce a novel ``Hallucinator\" module to reduce mutual information, increase the batch size of positive pairs, and improve representation quality. Subsequently, we extend the representation framework to agriculture and remote sensing, proposing spatial-temporal-aware architectures tailored to the unique characteristics of remote sensing data. Furthermore, we introduce the Extended Agriculture Vision dataset to address data scarcity issues and showcase the effectiveness of proposed representation frameworks. Furthermore, we demonstrate that the learned representations are powerful features for few-shot tasks in remote sensing and earth observation. We introduce GenCo, a generator-based representation learning framework that simultaneously pre-trains backbones and explores variants of feature samples. During fine-tuning, the auxiliary generator enriches the limited labeled data samples in the feature space. We validate the effectiveness of our method in enhancing few-shot learning performance on the Agriculture-Vision and EuroSAT datasets. Notably, our few-shot approach surpasses purely supervised training in both classification and semantic segmentation tasks trained over ten thousand images in the Agriculture-Vision Dataset. Lastly, we propose an intelligent nitrogen (N) management system utilizing deep reinforcement learning (RL) in conjunction with crop simulations through the Decision Support System for Agrotechnology Transfer (DSSAT). Initially, we framed the N management issue as an RL problem. Subsequently, we train management policies using deep Q-network and soft actor-critic algorithms, along with the Gym-DSSAT interface. This interface facilitates daily interactions between the simulated crop environment and RL agents. According to our experiments with maize crops in both Iowa and Florida, USA, the RL-trained policies surpass previous empirical methods."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An exploratory journey of representation learning’s enhancement, adaptation and related intelligent methods"]}]}],"canonical_facts":{"dc:contributor":["Hovakimyan, Naira","Salapaka, Srinivasa","Martin, Nicolas Federico","Wang, Yuxiong"],"dc:creator":["Wu, Jing"],"dc:date":["2024-05","2024-04-24"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Jing Wu, accepted the attached license on 2024-04-23 at 14:04.","The student, Jing Wu, submitted this Dissertation for approval on 2024-04-23 at 14:13.","This Dissertation was approved for publication on 2024-04-24 at 15:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20496 on 2024-09-16 at 00:35:29","Representation learning models employing Siamese structures have consistently demonstrated exceptional performance across various fields, including deep learning, computer vision, and natural language processing. Furthermore, the applicability of representation learning has broadened to encompass wider domains such as agriculture, remote sensing, and earth observation, which are significantly challenged by data scarcity. This dissertation aims to enhance the quality and adaptability of learned representations across these diverse application domains. Meanwhile, we have also expanded the scope of our research to a broader area of intelligent agricultural systems. Initially, we delve into contrastive representation learning within the general computer vision domain and introduce a novel ``Hallucinator\" module to reduce mutual information, increase the batch size of positive pairs, and improve representation quality. Subsequently, we extend the representation framework to agriculture and remote sensing, proposing spatial-temporal-aware architectures tailored to the unique characteristics of remote sensing data. Furthermore, we introduce the Extended Agriculture Vision dataset to address data scarcity issues and showcase the effectiveness of proposed representation frameworks. Furthermore, we demonstrate that the learned representations are powerful features for few-shot tasks in remote sensing and earth observation. We introduce GenCo, a generator-based representation learning framework that simultaneously pre-trains backbones and explores variants of feature samples. During fine-tuning, the auxiliary generator enriches the limited labeled data samples in the feature space. We validate the effectiveness of our method in enhancing few-shot learning performance on the Agriculture-Vision and EuroSAT datasets. Notably, our few-shot approach surpasses purely supervised training in both classification and semantic segmentation tasks trained over ten thousand images in the Agriculture-Vision Dataset. Lastly, we propose an intelligent nitrogen (N) management system utilizing deep reinforcement learning (RL) in conjunction with crop simulations through the Decision Support System for Agrotechnology Transfer (DSSAT). Initially, we framed the N management issue as an RL problem. Subsequently, we train management policies using deep Q-network and soft actor-critic algorithms, along with the Gym-DSSAT interface. This interface facilitates daily interactions between the simulated crop environment and RL agents. According to our experiments with maize crops in both Iowa and Florida, USA, the RL-trained policies surpass previous empirical methods."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124344"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jing Wu"],"dc:subject":["Representation Learning","Machine Learning","Intelligent Agriculture"],"dc:title":["An exploratory journey of representation learning’s enhancement, adaptation and related intelligent methods"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}