Development of an automated grass cutting machine for university field using internet of things/ Mary Jane G. Dieto, Kimberly R. Ibanez, Maria Fe D. Pole, and Maria Flor D. Pole.--
Material type:
TextPublication details: Manila: Technological University of the Philippines, 2024.Description: xiii, 140pages: 29cmContent type: - BTH QA 76.9 D54 2024
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Bachelor's Thesis CIT
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TUP Manila Library | Thesis Section-2nd floor | BTH QA 76.9 D54 2024 (Browse shelf(Opens below)) | c.1 | Not for loan | BTH0006281 |
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Bachelor's thesis
College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2024.
Includes bibliographic references and index.
This study develops an IoT-based automated grass-cutting machine to improve efficiency
and safety in maintaining the TUP-Manila field. Traditional methods require manual labor,
which is time-consuming and poses safety risks. The problems related to noise pollution
and gasoline-powered mower environmental damage along with intensive manual
procedures continue to persist as significant problems. Existing solutions, such as solar and
automated grass cutters, have limitations in range, features, and reliance on manual
operation. The study aims to develop a grass-cutting machine for TUP-Manila field with
the following objectives: (1) design a system that cuts and collects grass while
incorporating safety features such as a front blade cover and IoT-based camera monitoring;
(2) develop a mobile-controlled system using agile methodology; (3) test speed, cutting
precision, grass collection, and battery life under operating conditions; and (4) evaluate the
system based on ISO/IEC 25010:2023 standards. The development process followed an
agile methodology, consisting of planning, design, development, testing, deployment, and
review phases. The development followed an agile methodology with phases of planning,
design, development, testing, deployment, and review. The testing showed the prototype's
speed had minor discrepancies (76 m/min with 5.13% and 84 m/min with 1.18% deviation).
Cutting precision showed a 13.33% difference in grass height, while the collector bin
gathered 0.45 kg (75.86%) and 0.65 kg (79.07%) of grass from 76 m and 84 m,
respectively. Evaluation of the system based on quality criteria yielded a grand weighted
mean of 4.36 on a 5-point Likert scale in evaluations.
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