Sistem Monitoring Berat Tanaman Pada Farmbot Dengan Citra Digital Menggunakan Metode Yolo Dan Regresi Polinomial

Haikal, Fikri (2023) Sistem Monitoring Berat Tanaman Pada Farmbot Dengan Citra Digital Menggunakan Metode Yolo Dan Regresi Polinomial. Undergraduate thesis, Politeknik Negeri Jember.

	Text (Bab I Pendahuluan) Pendahuluan.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (532kB)
	Text (Abstract) Abstract.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (489kB)
	Text (Daftar Pustaka) Daftar Pustaka.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (527kB)
	Text (Laporan Lengkap) Laporan Lengkap.pdf Restricted to Registered users only Download (4MB) | Request a copy

Abstract

This research aims to monitor the growth of lettuce plants using a farmbot with a combined approach of YOLOv3 and third-order polynomial regression. YOLOv3 is employed for plant detection, while the polynomial regression calculates the estimated plant weight based on the pixel area generated by YOLOv3. Additionally, the study integrates a mobile application to monitor plant weight, visualize plant growth, and access daily historical growth data. The research results indicate an accuracy of 2.9% for polynomial regression and 98.6% for YOLOv3, confirming their effectiveness in weight prediction and plant detection. With positive implications for agriculture, this technology is expected to enhance the efficiency and management of plant growth, providing valuable information for farmers to make informed decisions and supporting increased productivity and sustainability in agriculture.

Actions (login required)

View Item