Insect Pest Detector Dengan Pengenalan Citra Hama Plutella Xylostella L. Pada Tanaman Sawi

Husna, Safitri Asma'ul (2021) Insect Pest Detector Dengan Pengenalan Citra Hama Plutella Xylostella L. Pada Tanaman Sawi. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Plutella xylostella L. merupakan salah satu jenis hama yang menyerang tanaman sawi, berbentuk silindris dan berwarna hijau kekuningan menyerupai warna hijau daun sawi. Hal ini menyebabkan petani mengalami kesulitan dalam mengenali jenis hama ini. Pendeteksian dilakukan melalui pengenalan citra. Pengenalan Plutella xylostella L. dirancang oleh komponen utama yaitu kamera Raspberry Pi NoIR dan mikroprosesor Raspberry Pi 3B. Metode Haar-Cascade digunakan sebagai pengolah data gambar menjadi dataset. Algoritma pemrograman dirancang dengan perangkat lunak OpenCV - Python. Hasil dari Tugas Akhir ini adalah sebuah prototipe yang diintegrasikan pada layar LCD sebagai GUI untuk visualisasi status hasil deteksi. Hasil dari penelitian yang dilakukan yakni tingkat deteksi memiliki nilai akurasi sebesar 73% dengan batas sampel maksimum 3 objek Plutella xylostella L. Jarak pengambilan gambar yang paling sesuai ialah 10cm dengan skala pengaturan pada range 35-400. Dengan adanya sistem ini, diharapkan dapat membantu petani dalam mengenali jenis hama sebagai acuan untuk pengendalian hama tanaman sawi.
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Plutella xylostella L. is one type of pest that attacks Brassica juncea L., cylindrical in shape and yellowish-green in color resembling the green color of Brassica juncea L. This causes farmers have difficulty recognizing this pest. Detection is done through image recognition. The introduction of Plutella xylostellaL. is designed by Raspberry Pi NoIR camera and Raspberry Pi 3B
microprocessor. Method Haar-Cascade is used to process image data. Programming algorithm is designed with OpenCV - Python. The result of this final project is a prototype that integrated on the LCD screen as a GUI to visualize
detection status. The result of detection rate reaches an accuracy 73% with a maximum sample limit of 3 objects. The most suitable shooting distance is 10cm
with a setting scale in the range of 35-400. With this system, it is hoped that can assist farmers in recognizing the type of pest as a reference for controlling pest.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Detektor Hama, Haar-Cascade, Image Processing, Pengenalan Citra Hama, Haar-Cascade, Image Processing, Pest Detector, Pest Image Recognition
Subjects: Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines.
Q Science > Q Science (General) > Q337.5 Pattern recognition systems
Q Science > QC Physics > QC100.5 Measuring instruments (General)
Q Science > QC Physics > QC451 Spectroscopy
S Agriculture > S Agriculture (General)
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques
T Technology > TA Engineering (General). Civil engineering (General) > TA1650 Face recognition. Optical pattern recognition.
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7882.P3 Pattern recognition systems
T Technology > TR Photography > TR260.7 Autofocus cameras
Divisions: Faculty of Vocational > Instrumentation Engineering
Depositing User: Safitri Asma'ul Husna
Date Deposited: 24 Aug 2021 01:14
Last Modified: 24 Aug 2021 01:14
URI: http://repository.its.ac.id/id/eprint/88550

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