Penerapan Arsitektur U-Net untuk Segmentasi Semantik Citra Buah dengan Latar Belakang Kompleks

Muh Jamil Jamil; Asiah Wati; Sitti Rahmah; Aldi Bastiatul Fawait

doi:10.30872/jurti.v10i1.26518

Penerapan Arsitektur U-Net untuk Segmentasi Semantik Citra Buah dengan Latar Belakang Kompleks

Muh Jamil Jamil, Asiah Wati, Sitti Rahmah, Aldi Bastiatul Fawait

Abstract

Perkembangan ilmu Computer Vision telah berhasil membuat terobosan baru di berbagai bidang, salah satunya adalah di bidang pertanian. Hal ini semakin membuka peluang bagi pertanian cerdas berbasis IoT untuk dapat diterapkan guna mengoptimalisasi pekerjaan petani. Salah satu langkah awal yang bisa diterapkan adalah segmentasi citra, tujuannya adalah untuk mendeteksi area tertentu seperti buah, daun dan objek lain yang memiliki oklusi dan latar belakang kompleks pada lingkungan perkebunan secara langsung. Penelitian yang dikerjakan mengusulkan metode segmentasi semantic berbasis Deep Learning yaitu dengan memadukan ketangguhan dari U-Net dan juga VGG16 dalam melakukan proses segementasi buah melon pada lingkungan perkebunan secara langsung. Model segmentasi yang dirancangan terbukti mampu melakukan proses segmentasi dengan sangat optimal dengan score loss minimum sebesar 0.0157 dan score IoU maksimum sebesar 0.9922 pada proses train dan testing model, selain itu model yang dirancang mampu bekerja secara optimal dengan score IoU sebesar 0.9893 pada validasi akhir model. Penelitian yang dikerjakan bukan hanya berkontribusi sebagai rujukan akademis terkait dengan penerapan computer vision pada bidang pertanian cerdas, tetapi juga dapat berkontribusi menciptakan dataset baru bagi penelitian lanjut di masa mendatang.

Keywords

U-Net; Deep Learning; Segmentasi; Melon; IoT

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DOI: http://dx.doi.org/10.30872/jurti.v10i1.26518

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