Augmented Reality (AR) for Precision Farming: Enhancing Farmer Decision-Making in Pest Control
Pest control in modern agriculture is a huge challenge where traditional ways are often labor intensive, error-prone, and require heavy use of pesticides that damage the environment. This was developed to respond to these challenges through an Augmented Reality (AR) enabled precision farming device...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01034.pdf |
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| Summary: | Pest control in modern agriculture is a huge challenge where traditional ways are often labor intensive, error-prone, and require heavy use of pesticides that damage the environment. This was developed to respond to these challenges through an Augmented Reality (AR) enabled precision farming device to help farmers in optimizing pest management. The combination of this system is to integrate a machine learning model with the data from IoT sensors and drone imaging to predict and visualize pest populations in real-time. Farmers will be able to see pest infestations through AR devices in the field for immediate and targeted responses to pest-related problems. In using the Random Forest model for this purpose, pesticide usage was decreased by about 30%, while levels of pest control remained constant. This approach shows a great improvement in resource efficiency and environmental sustainability. The AR precision farming system enhances decision-making and diminishes the need to use traditional pest control methods in agriculture that facilitate ecological practices. This allows the farmers to optimize the resource allocation, protect the crop health, and also higher overall agricultural productivity. The findings of the potential use of advanced technologies such as AR and IoT in the redesigning of pest management and nurturing of sustainable farming solutions. |
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| ISSN: | 2261-2424 |