SprayCraft: Graph-Based Route Optimization for Variable Rate Precision Spraying
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910747939831808 |
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| author | Kethineni, Kiran K. Mohanty, Saraju P. Kougianos, Elias Bhowmick, Sanjukta Rachakonda, Laavanya |
| author_facet | Kethineni, Kiran K. Mohanty, Saraju P. Kougianos, Elias Bhowmick, Sanjukta Rachakonda, Laavanya |
| contents | To efficiently manage plant diseases, Agriculture Cyber-Physical Systems (A-CPS) have been developed to detect and localize disease infestations by integrating the Internet of Agro-Things (IoAT). By the nature of plant and pathogen interactions, the spread of a disease appears as a focus with density of infected plants and intensity of infection diminishing outwards. This gradient of infection needs variable rate and precision pesticide spraying to efficiently utilize resources and effectively handle the diseases. This article, SprayCraft presents a graph based method for disease management A-CPS to identify disease hotspots and compute near optimal path for a spraying drone to perform variable rate precision spraying. It uses graph to represent the diseased locations and their spatial relation, Message Passing is performed over the graph to compute the probability of a location to be a disease hotspot. These probabilities also serve as disease intensity measures and are used for variable rate spraying at each location. Whereas, the graph is utilized to compute tour path by considering it as Traveling Salesman Problem (TSP) for precision spraying by the drone. Proposed method has been validated on synthetic data of locations of diseased locations in a farmland. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SprayCraft: Graph-Based Route Optimization for Variable Rate Precision Spraying Kethineni, Kiran K. Mohanty, Saraju P. Kougianos, Elias Bhowmick, Sanjukta Rachakonda, Laavanya Other Computer Science Systems and Control To efficiently manage plant diseases, Agriculture Cyber-Physical Systems (A-CPS) have been developed to detect and localize disease infestations by integrating the Internet of Agro-Things (IoAT). By the nature of plant and pathogen interactions, the spread of a disease appears as a focus with density of infected plants and intensity of infection diminishing outwards. This gradient of infection needs variable rate and precision pesticide spraying to efficiently utilize resources and effectively handle the diseases. This article, SprayCraft presents a graph based method for disease management A-CPS to identify disease hotspots and compute near optimal path for a spraying drone to perform variable rate precision spraying. It uses graph to represent the diseased locations and their spatial relation, Message Passing is performed over the graph to compute the probability of a location to be a disease hotspot. These probabilities also serve as disease intensity measures and are used for variable rate spraying at each location. Whereas, the graph is utilized to compute tour path by considering it as Traveling Salesman Problem (TSP) for precision spraying by the drone. Proposed method has been validated on synthetic data of locations of diseased locations in a farmland. |
| title | SprayCraft: Graph-Based Route Optimization for Variable Rate Precision Spraying |
| topic | Other Computer Science Systems and Control |
| url | https://arxiv.org/abs/2412.12176 |