KLASIFIKASI PENYAKIT DAN KONDISI KESEHATAN TANAMAN CABAI BERBASIS DEEP LEARNING SEBAGAI PENDUKUNG SMART NURSERY PERTANIAN BERKELANJUTAN
DOI:
https://doi.org/10.55123/storage.v5i3.9469Keywords:
deep learning, klasifikasi tanaman, cabai, MobileNetV3Small, smart nurseryAbstract
Tanaman cabai (Capsicum annuum L.) merupakan komoditas hortikultura bernilai ekonomi tinggi yang memerlukan pemantauan kesehatan tanaman secara cepat dan akurat sejak tahap pembibitan. Proses identifikasi penyakit dan kondisi tanaman yang masih dilakukan secara manual memiliki keterbatasan karena bergantung pada pengalaman petani dan berpotensi menyebabkan keterlambatan pengendalian. Penelitian ini bertujuan mengembangkan model klasifikasi kondisi kesehatan tanaman cabai berbasis deep learning sebagai pendukung sistem smart nursery pertanian berkelanjutan. Metode penelitian dilakukan melalui tahapan pengumpulan dataset citra daun cabai, preprocessing, augmentasi data, pembangunan model, pelatihan, pengujian, dan evaluasi performa. Dataset terdiri dari lima kategori kondisi tanaman, yaitu healthy, leaf curl, leaf spot, whitefly, dan yellowish, dengan total 500 citra yang dibagi menjadi data pelatihan, validasi, dan pengujian. Tiga model dibandingkan, yaitu Convolutional Neural Network (CNN), MobileNetV3Small, dan EfficientNet-B0 menggunakan parameter evaluasi accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa MobileNetV3Small memberikan performa terbaik dengan akurasi 88%, precision 0,91, recall 0,88, dan F1-score 0,88, serta memiliki jumlah parameter paling rendah dibandingkan model lainnya. Model tersebut berpotensi diterapkan pada sistem smart nursery berbasis kamera dan perangkat komputasi terbatas untuk mendukung deteksi dini kesehatan bibit cabai secara cepat, objektif, dan berkelanjutan.
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Copyright (c) 2026 Titan Attariq Al Fatah, Muhamad Fuat Asnawi, Restu Rahma Riandhita, Panggah Dwi Santoso, Zayyana Maulida, Riskha Suheila Kirmalani, Nurrohmat Furkon, Ahmad Latif Hendrawan, Aurielia Chandra Febrina, Gennaro Wibisana, Febri Dwi Saputra, Rafif Phalosa, Muhammad Irvan Asy'ari, Syahrul Maulana Rozaki, Dzidni Imron Fadhilah, Muhammad Nabil Al Faqih
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