Deep Learning Berbasis U-Net untuk Segmentasi Citra Daun Kakao dalam Deteksi Dini Penyakit Tanaman
DOI:
https://doi.org/10.54065/artificial.2022Keywords:
Daun Kakao, Deep Learning, Deteksi Dini Penyakit, Segmentasi Citra, U-NetAbstract
Penyakit pada tanaman kakao dapat memengaruhi kondisi dan produktivitas tanaman sehingga diperlukan pendekatan berbasis pengolahan citra untuk mendukung proses deteksi secara lebih efektif. Salah satu tahap penting dalam analisis citra daun adalah segmentasi untuk memisahkan objek daun dari latar belakang sehingga informasi area pada citra dapat dianalisis lebih lanjut. Penelitian ini bertujuan menerapkan metode deep learning berbasis U-Net untuk melakukan segmentasi citra daun kakao sebagai tahap awal dalam mendukung deteksi dini penyakit tanaman. Data yang digunakan berupa citra digital daun kakao beserta ground-truth mask yang dianotasi secara piksel. Tahapan penelitian meliputi prapemrosesan dengan resize citra menjadi 224×224 piksel, normalisasi nilai piksel, pembentukan binary mask, pembagian dataset menjadi data training, validation, dan testing dengan proporsi 80:10:10, serta pelatihan model U-Net menggunakan optimizer Adam, Binary Crossentropy, 10 epoch, dan batch size 8. Evaluasi dilakukan menggunakan Accuracy, Intersection over Union (IoU), dan Dice Coefficient. Hasil pengujian menunjukkan nilai Accuracy sebesar 98%, IoU sebesar 91%, dan Dice Coefficient sebesar 94%. Pada salah satu citra pengujian, area yang terindikasi mengalami penyakit sebesar 20,33% dari keseluruhan area daun. Hasil tersebut menunjukkan bahwa U-Net mampu melakukan segmentasi citra daun kakao dengan baik dan dapat menjadi tahap pendukung dalam pengembangan sistem deteksi dini penyakit tanaman. Penelitian selanjutnya dapat mengintegrasikan hasil segmentasi dengan metode klasifikasi untuk mengidentifikasi jenis penyakit secara otomatis.
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