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All Journal Bulletin of Electrical Engineering and Informatics JURNAL PERTANIAN PRESISI (JOURNAL OF PRECISION AGRICULTURE)
Risnawati Risnawati
Gunadarma University
Agriculture, Biological Sciences & Forestry Electrical & Electronics Engineering

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DETEKSI DAN PERHITUNGAN JUMLAH POHON SAWIT SECARA OTOMATIS PADA REMOTE SENSING IMAGERY MENGGUNAKAN ALGORITMA YOLOV11n Risnawati Risnawati; Ummu Kalsum; Edi Minaji Pribadi; Ahmad Yozar Perkasa; Adinda Nurul Huda Manurung
Jurnal Pertanian Presisi (Journal of Precision Agriculture) Vol. 10 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/jpp.2026.v10i1.230

Abstract

Sawit merupakan komoditi industri penghasil minyak yang saat ini dibudidayakan dalam skala areal perkebunan yang luas. Tanaman sawit skala luas butuh teknologi agar bisa berkelanjutan dan menghasilkan capaian produksi yang maksimal. Pemantauan terhadap tanaman sawit perlu dilakukan seperti untuk menentukan jumlah pohon sawit pada areal yang luas dibutuhkan guna memprediksi kebutuhan pupuk dan pestisida yang diperlukan serta bisa mengetahui jumlah tanaman sawit yang masih tumbuh dan berproduksi. Tujuan penelitian ini adalah optimasi hyperparameter arsitektur model YOLOv11n untuk mendeteksi tanaman pohon sawit dan menghitung jumlah pohon sawit pada citra hasil remote sensing. Deteksi pohon sawit menggunakan model YOLOv11n menghasilkan nilai presisi, recall, mAP50 dan mAP50-90 masing-masing sebesar 92.3%, 96.8%, 97.3% dan 62.4%. Perhitungan pohon sawit secara otomatis pada dataset citra remote sensing menggunakan perangkat QGIS diperoleh sebesar 3867 pohon sawit. Hasil perhitungan pohon sawit tersebut membuktikan bahwa YOLOv11n mampu mendeteksi sekaligus mampu menghitung jumlah pohon sawit yang terdapat pada luasan area dalam citra spasial pohon sawit hasil akuisisi dari remote sensing.
Deep learning based identification of Crocidolomia pavonana larvae on mustard plants using Grad-CAM Diana Tri Susetianingtias; Sarifuddin Madenda; Risnawati Risnawati; Maukar Maukar; Eka Patriya; Rodiah Rodiah
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11343

Abstract

Mustard greens are an important vegetable commodity, but their production is often affected by pest attacks, especially the cabbage worm Crocidolomia pavonana (C. pavonana). The larvae damage leaf tissues and cause significant yield losses, while chemical control is often ineffective due to differences in insecticide sensitivity across larval instars. This study proposes a deep learning based classification approach combined with gradient weighted class activation mapping (Grad-CAM) to identify larval instars of C. pavonana on mustard plants. A dataset of 684 images covering instars 1 to 4 was collected from laboratory rearing and field observations, then processed using resizing and augmentation techniques and divided into training, validation, and testing sets with an 8 to 1 to 1 ratio. Two convolutional neural network (CNN) models, visual geometry group 19 (VGG19), and Xception, were implemented with additional fully connected layers. The VGG19 model achieved 94.20% accuracy and outperformed Xception. Grad-CAM successfully highlighted larval regions and supported visual interpretation. The results show that the proposed method can improve pest identification accuracy and support more effective pest management.
Co-Authors Ahmad Yozar Perkasa Edi Minaji Pribadi Eka Patriya Manurung, Adinda Nurul Huda Maukar Maukar Rodiah Rodiah Sarifuddin Madenda Susetianingtias, Diana Tri Ummu Kalsum