DOI : https://doi.org/10.29165/ajarcde.v10i3.1103

AI-Powered Image Recognition for Early Detection of Pests in Medicinal Crops

Siti Astuti (1), Bayu Dwi Apri Nugroho (2), Anisa Khoiriyah (3), Endah Puspitojati (4)
(1) Agricultural Development Polytechnic of Yogyakarta Magelang, Yogyakarta, Indonesia
(2) Gadjah Mada University. Indonesia
(3) Agricultural Development Polytechnic of Yogyakarta Magelang, Yogyakarta, Indonesia
(4) Agricultural Development Polytechnic of Yogyakarta Magelang, Yogyakarta, Indonesia
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How to cite (AJARCDE) :
Astuti, S., Nugroho, B. D. A., Khoiriyah, A., & Puspitojati, E. (2026). AI-Powered Image Recognition for Early Detection of Pests in Medicinal Crops . AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment), 10(3), 299–304. https://doi.org/10.29165/ajarcde.v10i3.1103
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Dalam beberapa tahun terakhir, perkembangan Kecerdasan Buatan (AI) sangat pesat di sektor pertanian, bahkan memainkan peran penting dalam meningkatkan produktivitas komoditas pertanian. AI merupakan bagian dari solusi deteksi dini untuk meminimalkan risiko serangan hama dan penyakit yang parah. Penelitian ini bertujuan untuk menunjukkan akurasi dan kerahasiaan data untuk membangun model pengenalan citra gejala serangan hama dan penyakit pada tanaman biofarmasi. Metode yang digunakan adalah observasi dan dokumentasi, serta sampel tanaman serai wangi, yang merupakan salah satu komoditas tanaman biofarmasi potensial saat ini. Langkah-langkah memastikan pengumpulan data untuk identifikasi citra guna akurasi dan efektivitas sistem meliputi pengambilan data citra, identifikasi, penandaan foto, dan pembuatan algoritma. Hasil pengumpulan data menggunakan pengenalan citra gejala serangan pada tanaman serai wangi sebagai sampel tanaman biofarmasi, yaitu Valanga sp, Culvilaria sp, dan Fusarium sp. Algoritma dapat mengenali 100% foto uji untuk belalang dan gejala Culvilaria sp. Tingkat kepercayaan algoritma dalam mengenali data menunjukkan rata-rata >50%. Teknologi pengenalan gambar dapat menyajikan data dasar algoritmik untuk mendiagnosis gejala serangan hama dan penyakit dengan akurasi dan kepercayaan yang tinggi. Kontribusi penelitian ini secara teoritis memberikan gambaran yang jelas tentang langkah-langkah pengumpulan data dan juga menawarkan hasil data yang akurat, tepat, dan cepat berdasarkan kenyataan di lapangan sehingga dapat digunakan sebagai referensi dalam mendiagnosis hama dan penyakit tanaman.

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