| dc.contributor.advisor | Priandana, Karlisa | |
| dc.contributor.author | Ansyafa, Khairunnissa Zahran | |
| dc.date.accessioned | 2025-07-26T06:24:54Z | |
| dc.date.available | 2025-07-26T06:24:54Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/165922 | |
| dc.description.abstract | Teknologi modern semakin berperan dalam pertanian, terutama pada budidaya hortikultura seperti pakcoy yang rentan hama namun bernilai ekonomi tinggi. Penyemprotan pestisida secara manual menghadapi banyak tantangan, sehingga dibutuhkan pendekatan berbasis teknologi. Penelitian ini mengembangkan robot penyemprot pestisida berbasis AI dengan algoritma YOLO untuk mendeteksi tanaman pakcoy. Robot bergerak mengikuti jalur (line follower) dan menyemprotkan pestisida hanya saat tanaman terdeteksi oleh kamera. Sistem ini menggunakan Raspberry Pi 5 untuk pemrosesan citra dan ESP untuk mengontrol nozzle penyemprotan. Hasil penelitian menunjukkan sistem mampu mendeteksi tanaman pakcoy dalam berbagai kondisi, menerima koordinat tanaman dan melakukan penyemprotan secara tepat sasaran. Sistem ini terbukti dapat mengotomatisasi proses penyemprotan, mengurangi ketergantungan tenaga kerja, serta menurunkan risiko paparan pestisida bagi petani. | |
| dc.description.abstract | Modern technology assumes an increasingly significant role in agriculture, including the cultivation of high-economic-value horticultural crops that are susceptible to pest damage. Conventional pesticide spraying often presents challenges, necessitating a technology-driven approach to enhance the spraying process. This research develops an AI-powered pesticide sprayer robot utilizing the YOLO algorithm for plant detection. The robot operates as a line follower, spraying pesticides only when a plant is detected by its camera. The system leverages a Raspberry Pi 5 for image processing and an ESP32 microcontroller to control the nozzle, ensuring more precise and organized spraying. The research focused on applying AI to detect pakcoy plants in cultivation, rather than classifying pest types. The results show the system successfully identified pakcoy plants under various conditions, transmitted their coordinates, and accurately sprayed pesticides towards them. This demonstrates the system's ability to automate precise spraying, reduce reliance on human labor, and minimize farmers' exposure to pesticides. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title | Penerapan AI Dalam Pendeteksian Daun Pakcoy Untuk Robot Penyiram Pestisida Berbasis Line Follower | id |
| dc.title.alternative | | |
| dc.type | Tugas Akhir | |
| dc.subject.keyword | Artificial Intelligence | id |
| dc.subject.keyword | horticulture | id |
| dc.subject.keyword | Line Follower Robot | id |
| dc.subject.keyword | Pakcoy Detection | id |
| dc.subject.keyword | YOLO Algorithm | id |
