Date of Award

6-11-2024

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2024

Department

Computer Science and Engineering

First Advisor

Behnam Dezfouli

Second Advisor

Shiva Jahangiri

Abstract

Beehive monitoring plays a major role in ensuring the health of beehives by checking for overpopulation or underpopulation within a hive. Beehive monitoring provides beekeepers with the opportunity to take action and save the hive before the problem becomes irreversible. Most solutions are too expensive for everyday beekeepers and lack elements of sustainability, making it impractical for small scale beekeepers. In this thesis, we propose a solution to this problem, demonstrating its sustainability and user-friendliness, which enables us to effectively reach a larger consumer market. We support these claims through the use of sustainable systems such as using a solar panel coupled with a rechargeable battery and incorporating deep-sleep capabilities into the system’s low-power embedded system (ESP32) which is connected to a camera. The ESP32 sends images to a Raspberry Pi, which performs image processing using a machine learning model and transmits the processed images to the cloud. We present a system architecture diagram describing how these systems are integrated as well as how other measures, such as security and single sign-on, are implemented to ensure the integrity of the solution. The system tests conducted in the field show that the machine learning model yields a mean average precision (MAP) score of 52.2, compared to the benchmark score of 53.7, ensuring accurate, real-time monitoring utilizing a low-power system.

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