Integrasi Smart Camera dan AI untuk Mendukung Agribisnis Kopi Berkelanjutan di Wilayah Pedesaan Indonesia
DOI:
https://doi.org/10.31294/abditeknika.v5i2.9602Keywords:
Smart Camera, Biji Kopi, Sortir OtomatisAbstract
Kendala utama agribisnis kopi pedesaan di Lembah Dilem Wilis, Trenggalek, adalah kualitas biji yang tidak konsisten akibat sortir manual. Sistem sortir otomatis berbasis Smart Camera dan AI dikembangkan untuk klasifikasi real-time biji kopi berdasarkan visual seperti warna dan ukuran. Dirancang dengan Raspberry Pi, sensor kamera, dan algoritma CNN , penerapannya melibatkan kolaborasi peneliti dan petani lokal secara partisipatif. Kami menggunakan 500 data latih yang diakuisisi dari aplikasi untuk pelatihan model CNN kami. Uji lapangan menunjukkan sistem ini mengurangi waktu sortir dari 45 menjadi 15 menit per kg, meningkatkan akurasi seleksi dari 75% menjadi 94%, dan melipatgandakan produktivitas harian. Analisis confusion matrix heatmap mengonfirmasi akurasi klasifikasi tinggi , dan sensor PZEM menunjukkan keandalan pemantauan daya. Meskipun meningkatkan efisiensi, tantangan adopsi petani kecil dan variasi kondisi biji kopi menjadi area pengembangan lebih lanjut. Evaluasi kuantitatif sebelum dan sesudah implementasi, menggunakan sampel biji kopi lokal, menegaskan bahwa integrasi teknologi ini meningkatkan efisiensi, mutu produksi, dan memberdayakan petani menuju agribisnis berkelanjutan.
The primary challenge for rural coffee agribusiness in Dilem Wilis Valley, Trenggalek, is inconsistent bean quality due to manual sorting. An automated sorting system based on Smart Camera and AI was developed for real-time classification of coffee beans based on visual parameters like color and size. Designed with a Raspberry Pi, camera sensor, and CNN algorithm , its implementation involved participatory collaboration between researchers and local farmers. We used 500 training data acquired from the application for our CNN model training. Field trials showed the system reduced sorting time from 45 to 15 minutes per kg, increased selection accuracy from 75% to 94%, and doubled daily productivity. Confusion matrix heatmap analysis confirmed high classification accuracy , and PZEM sensors demonstrated reliable power monitoring. While enhancing efficiency, challenges in small-scale farmer adoption and varied bean conditions present areas for further development. Quantitative evaluation before and after implementation, using local coffee bean samples, affirmed that this technology integration boosts efficiency, product quality, and empowers farmers towards sustainable agribusiness.
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Copyright (c) 2025 Aripriharta, Titi Mutiara Kiranawati, Siti Zubaidah, Mazarina Devi, Nur Fadhila Rasyida Salwa, Sasmita Bagus Sang Kesuma Ananta, Muhammad Cahyo Bagaskoro (Author)
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