Penerapan Computer Vision pada Smart Agriculture untuk pengenalan Tingkat Kematangan Buah
Keywords:
computer visionAbstract
Penelitian Pertanian cerdas berkembang pesat seiring meningkatnya kebutuhan teknologi otomatisasi dalam sektor pertanian, khususnya pada proses identifikasi tingkat kematangan buah yang masih banyak dilakukan secara manual. Penggunaan computer vision menjadi solusi yang efektif karena mampu melakukan analisis citra secara cepat, akurat, dan konsisten. Tujuan: Penelitian ini bertujuan untuk menerapkan metode computer vision pada sistem pertanian cerdas untuk mengidentifikasi tingkat kematangan buah berdasarkan warna, tekstur, dan bentuk objek. Metode: Penelitian menggunakan pendekatan kuantitatif dengan tahapan pengumpulan dataset citra buah, preprocessing gambar, ekstraksi fitur warna RGB dan HSV, serta klasifikasi menggunakan algoritma Convolutional Neural Network. Dataset diperoleh dari pengambilan gambar buah pada beberapa tingkat kematangan menggunakan kamera digital dengan kondisi pencahayaan yang terkontrol. Hasil: Sistem mampu mengidentifikasi tingkat kematangan buah dengan tingkat akurasi sebesar 93,4%. Proses klasifikasi menunjukkan bahwa fitur warna menjadi parameter paling dominan dalam menentukan tingkat kematangan buah dibandingkan tekstur dan bentuk. Pengujian sistem dilakukan menggunakan data latih dan data uji dengan hasil kinerja yang stabil pada berbagai kondisi pencahayaan. Kesimpulan: Penerapan computer vision pada pertanian pintar terbukti mampu meningkatkan efisiensi proses identifikasi tingkat kematangan buah secara otomatis dan real-time. Sistem ini berpotensi diterapkan pada industri pertanian modern untuk membantu petani meningkatkan kualitas hasil panen dan mengurangi kesalahan penghapusan manual. Penelitian selanjutnya dapat mengembangkan sistem berbasis Internet of Things dan integrasi sensor otomatis untuk meningkatkan kinerja sistem secara lebih adaptif
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