{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/88145"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/88145","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A data-driven approach to soil moisture collection and prediction using a wireless sensor network and machine learning techniques","abstract":"DSpace SAF Submission Ingestion Package generated from Vireo submission #8288 on 2015-09-29 at 14:58:34","abstract_html":"DSpace SAF Submission Ingestion Package generated from Vireo submission #8288 on 2015-09-29 at 14:58:34","abstract_has_math":false,"creators":["Hong, Zhihao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Iyer, Ravishankar K.","Kalbarczyk, Zbigniew T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-29T20:49:30Z","date_published":"2015-09-29T20:49:30Z","updated_at":"2026-07-22T22:26:31Z","subjects":["soil moisture","data driven","wireless sensor network","machine learning"],"languages":["en"],"rights":["Copyright 2015 Zhihao Hong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/88145","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Iyer, Ravishankar K.","Kalbarczyk, Zbigniew T."]},{"key":"dc:creator","label":"Author","values":["Hong, Zhihao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-29T20:49:30Z","2017-09-30T09:15:24Z","2015-08","2015-06-18","2015-8"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["soil moisture","data driven","wireless sensor network","machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Zhihao Hong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/88145"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["DSpace SAF Submission Ingestion Package generated from Vireo submission #8288 on 2015-09-29 at 14:58:34","Agriculture has been one of the most underinvestigated areas in technology, and the development of Precision Agriculture is still in its early stages. This thesis proposes a data-driven methodology that aims to address some of the current problems in Precision Agriculture development. Soil moisture, a key factor in the crop growth cycle, is selected as an example to demonstrate the effectiveness of our data-driven approach. The success of the data-driven approach depends on two factors: (1) the quality of the data gathered and (2) the effectiveness of its analysis and interpretation. Previous studies have focused on addressing these factors separately, by either developing hardware for collecting soil moisture data or building efficient data analysis models. In our work, we take a holistic approach by addressing problems on both ends and designing an integrated system for Precision Agriculture that uses a wireless sensor network and machine learning techniques. On the collection side, a reactive wireless sensor node is developed that aims to capture the dynamics of soil moisture while sampling at relatively low frequency to save energy. The sensor node dynamically adjusts its sampling frequency based on soil moisture readings and can be easily configured to meet the specific needs applications. The hardware is prototyped using MicaZ mote and VH400 soil moisture sensor. On the data analysis side, a site-specific soil moisture prediction framework is proposed based on models generated by the statistically sound machine learning techniques SVM (support vector machine) and RVM (relevance vector machine). The framework can integrate inputs from other reliable data sources to improve its accuracy. The proposed framework is evaluated under a historical dataset on 9 sites across Illinois. It achieves low error rates (15%) and high correlations (95%) between predicted values and actual values when forecasting soil moisture about 2 weeks ahead.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-08-01","The student, Zhihao Hong, accepted the attached license on 2015-06-17 at 22:21.","The student, Zhihao Hong, submitted this Thesis for approval on 2015-06-17 at 22:23.","This Thesis was approved for publication on 2015-06-18 at 10:12.","Made available in DSpace on 2015-09-29T20:49:30Z (GMT). No. of bitstreams: 2 HONG-THESIS-2015.pdf: 2030782 bytes, checksum: b4b1fad8a29c86623136f50f2ab7936e (MD5) LICENSE.txt: 4208 bytes, checksum: dabf98c5e51e5e6792bd9560fb6e50ec (MD5) Previous issue date: 2015-06-18","Embargo set by: Seth Robbins for item 89425 Lift date: 2017-09-29T20:50:34Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 89425 on 2017-09-30T09:15:24Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A data-driven approach to soil moisture collection and prediction using a wireless sensor network and machine learning techniques"]}]}],"canonical_facts":{"dc:contributor":["Iyer, Ravishankar K.","Kalbarczyk, Zbigniew T."],"dc:creator":["Hong, Zhihao"],"dc:date":["2015-09-29T20:49:30Z","2017-09-30T09:15:24Z","2015-08","2015-06-18","2015-8"],"dc:description":["DSpace SAF Submission Ingestion Package generated from Vireo submission #8288 on 2015-09-29 at 14:58:34","Agriculture has been one of the most underinvestigated areas in technology, and the development of Precision Agriculture is still in its early stages. This thesis proposes a data-driven methodology that aims to address some of the current problems in Precision Agriculture development. Soil moisture, a key factor in the crop growth cycle, is selected as an example to demonstrate the effectiveness of our data-driven approach. The success of the data-driven approach depends on two factors: (1) the quality of the data gathered and (2) the effectiveness of its analysis and interpretation. Previous studies have focused on addressing these factors separately, by either developing hardware for collecting soil moisture data or building efficient data analysis models. In our work, we take a holistic approach by addressing problems on both ends and designing an integrated system for Precision Agriculture that uses a wireless sensor network and machine learning techniques. On the collection side, a reactive wireless sensor node is developed that aims to capture the dynamics of soil moisture while sampling at relatively low frequency to save energy. The sensor node dynamically adjusts its sampling frequency based on soil moisture readings and can be easily configured to meet the specific needs applications. The hardware is prototyped using MicaZ mote and VH400 soil moisture sensor. On the data analysis side, a site-specific soil moisture prediction framework is proposed based on models generated by the statistically sound machine learning techniques SVM (support vector machine) and RVM (relevance vector machine). The framework can integrate inputs from other reliable data sources to improve its accuracy. The proposed framework is evaluated under a historical dataset on 9 sites across Illinois. It achieves low error rates (15%) and high correlations (95%) between predicted values and actual values when forecasting soil moisture about 2 weeks ahead.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-08-01","The student, Zhihao Hong, accepted the attached license on 2015-06-17 at 22:21.","The student, Zhihao Hong, submitted this Thesis for approval on 2015-06-17 at 22:23.","This Thesis was approved for publication on 2015-06-18 at 10:12.","Made available in DSpace on 2015-09-29T20:49:30Z (GMT). No. of bitstreams: 2 HONG-THESIS-2015.pdf: 2030782 bytes, checksum: b4b1fad8a29c86623136f50f2ab7936e (MD5) LICENSE.txt: 4208 bytes, checksum: dabf98c5e51e5e6792bd9560fb6e50ec (MD5) Previous issue date: 2015-06-18","Embargo set by: Seth Robbins for item 89425 Lift date: 2017-09-29T20:50:34Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 89425 on 2017-09-30T09:15:24Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/88145"],"dc:language":["en"],"dc:rights":["Copyright 2015 Zhihao Hong"],"dc:subject":["soil moisture","data driven","wireless sensor network","machine learning"],"dc:title":["A data-driven approach to soil moisture collection and prediction using a wireless sensor network and machine learning techniques"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:31Z"}