New Technology Architecture and Strategy for Early Crop Disease Detection
New approaches are required to meet the challenge of devastating disease outbreaks in global agriculture. In particular, systemic, whole-plant methods of early crop disease detection are needed to effectively manage crop diseases that have long asymptomatic period and localized infection, such as t...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143218 |
| _version_ | 1811083063400071168 |
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| author | Robinson, Maxwell T. |
| author2 | de Weck, Olivier L |
| author_facet | de Weck, Olivier L Robinson, Maxwell T. |
| author_sort | Robinson, Maxwell T. |
| collection | MIT |
| description | New approaches are required to meet the challenge of devastating disease outbreaks in global agriculture. In particular, systemic, whole-plant methods of early crop disease detection are needed to effectively manage crop diseases that have long asymptomatic period and localized infection, such as the devastating citrus disease, Huanglongbing (HLB). In this thesis, we use system thinking to develop new crop disease detection technology that diagnoses disease by sensing plant-released volatile organic compounds (VOCs). We then recommend strategy for deployment of this detection technology. First, we demonstrate a new VOCs sensors architecture—humidity-initiated gas (HIG) sensors. HIG sensors employ water drawn from a humid environment to sense VOCs at low concentrations. We construct and evaluate two HIG sensor variants—Type I and Type II—and find that HIG sensors, particularly Type II sensors, address key requirements for field detection of crop disease. Type II sensors achieve <2 min response time and 5 ppb sensor limit of detection for geranyl acetone, a VOC downregulated during asymptomatic HLB. Second, we formulate and model early detection technology architecture that incorporates VOCs sensors. We then select and prototype a preferred detection technology architecture, and find that this prototype successfully detects and distinguishes between different plant VOC profiles. The preferred architecture incorporates VOCs sensors, gas chromatography, and a pre-concentrator, and is estimated to provide <1 ppt limit of detection for GA. Finally, we create a decision model for flexible deployment of crop disease detection technology under uncertainty in crop disease characteristics, including time of outbreak, initial outbreak magnitude, and contact rate. We use the model to select preferred decision rules for detection technology deployment that balance value of detection, cost of deployment, and feasibility for the particular case of HLB in California. Under severe HLB conditions, we estimate our preferred decision rules paired with existing mitigation methods will yield $83M in value to the California citrus industry. |
| first_indexed | 2024-09-23T12:19:39Z |
| format | Thesis |
| id | mit-1721.1/143218 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:19:39Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1432182022-06-16T03:30:54Z New Technology Architecture and Strategy for Early Crop Disease Detection Robinson, Maxwell T. de Weck, Olivier L Haji, Maha System Design and Management Program. New approaches are required to meet the challenge of devastating disease outbreaks in global agriculture. In particular, systemic, whole-plant methods of early crop disease detection are needed to effectively manage crop diseases that have long asymptomatic period and localized infection, such as the devastating citrus disease, Huanglongbing (HLB). In this thesis, we use system thinking to develop new crop disease detection technology that diagnoses disease by sensing plant-released volatile organic compounds (VOCs). We then recommend strategy for deployment of this detection technology. First, we demonstrate a new VOCs sensors architecture—humidity-initiated gas (HIG) sensors. HIG sensors employ water drawn from a humid environment to sense VOCs at low concentrations. We construct and evaluate two HIG sensor variants—Type I and Type II—and find that HIG sensors, particularly Type II sensors, address key requirements for field detection of crop disease. Type II sensors achieve <2 min response time and 5 ppb sensor limit of detection for geranyl acetone, a VOC downregulated during asymptomatic HLB. Second, we formulate and model early detection technology architecture that incorporates VOCs sensors. We then select and prototype a preferred detection technology architecture, and find that this prototype successfully detects and distinguishes between different plant VOC profiles. The preferred architecture incorporates VOCs sensors, gas chromatography, and a pre-concentrator, and is estimated to provide <1 ppt limit of detection for GA. Finally, we create a decision model for flexible deployment of crop disease detection technology under uncertainty in crop disease characteristics, including time of outbreak, initial outbreak magnitude, and contact rate. We use the model to select preferred decision rules for detection technology deployment that balance value of detection, cost of deployment, and feasibility for the particular case of HLB in California. Under severe HLB conditions, we estimate our preferred decision rules paired with existing mitigation methods will yield $83M in value to the California citrus industry. S.M. 2022-06-15T13:04:20Z 2022-06-15T13:04:20Z 2022-02 2022-03-18T16:29:15.103Z Thesis https://hdl.handle.net/1721.1/143218 0000-0003-1608-0589 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Robinson, Maxwell T. New Technology Architecture and Strategy for Early Crop Disease Detection |
| title | New Technology Architecture and Strategy for Early Crop Disease Detection |
| title_full | New Technology Architecture and Strategy for Early Crop Disease Detection |
| title_fullStr | New Technology Architecture and Strategy for Early Crop Disease Detection |
| title_full_unstemmed | New Technology Architecture and Strategy for Early Crop Disease Detection |
| title_short | New Technology Architecture and Strategy for Early Crop Disease Detection |
| title_sort | new technology architecture and strategy for early crop disease detection |
| url | https://hdl.handle.net/1721.1/143218 |
| work_keys_str_mv | AT robinsonmaxwellt newtechnologyarchitectureandstrategyforearlycropdiseasedetection |