Judul: Deep Learning Model YOLO for Detecting the Success of Rice (Oryza sativa) Planting Based on Unmanned Aerial Vehicle Image
Abstrak: Deteksi keberhasilan tanam padi (Oryza sativa) pada fase awal budidaya perlu dilakukan secara ketat agar dapat dimanfaatkan untuk kegiatan penyulaman, pengendalian hama, pengendalian penyakit, prediksi kebutuhan pupuk, dan perlakuan pemeliharaan tanaman yang tepat. Citra tangkapan dari Unmanned Aerial Vehicle (UAV) sangat potensial digunakan untuk mengamati lahan padi yang luas dengan bantuan algoritma kecerdasan buatan. Penelitian ini bertujuan mengembangkan model deep learning untuk mendeteksi tegakan padi di sawah pada fase awal budidaya menggunakan citra RGB beresolusi tinggi hasil tangkapan UAV. Penelitian ini menggunakan deep learning dengan algoritma YOLOv5 yang terdiri dari tahapan: (1) pengumpulan dataset, (2) pelabelan dataset, (3) training dataset, (4) uji model hasil training, dan (5) implementasi model. Training deep learning terhadap 9.677 dataset dilakukan dengan menggunakan algoritma YOLOv5s, YOLOv5m, dan YOLOv5l. Akurasi dari model deep learning menggunakan algoritma YOLOv5s, YOLOv5m, dan YOLOv5l masing-masing sebesar 89,35%; 91,29%; dan 79,43%. Nilai presisi yang didapatkan sebesar 99,90%; 99,80%; dan 99,89%. Sedangkan nilai recall yang didapatkan sebesar 89,43%; 91,46%; dan 79,50%. Hasil tersebut menunjukkan model deep learning yang telah dikembangkan mampu mendeteksi tegakan padi dengan akurasi tinggi dan presisi. Model dapat digunakan untuk mendeteksi padi pada fase awal budidaya dengan user interface berbasis web yang diimplementasikan dengan bahasa pemrograman Pyhton pada komputer lokal., Detection the success of rice (Oryza sativa) planting in the early stages of cultivation are important for embroidery activities, pest control, disease control, prediction of fertilizer needs, and proper plant maintenance treatment. The captured image from the Unmanned Aerial Vehicle (UAV) coupled with artificial intelligence are potential to observe large scale of rice fields. The objective of this study is to develop a deep learning model to detect rice stands using high resolution RGB imagery captured by UAV in the early stages of paddy. Deep learning with the YOLOv5 algorithm was used to develop the model that consists of the following stages: (1) dataset collection, (2) dataset labeling, (3) dataset training, (4) model testing, and (5) model implementation. Deep learning was employed to train 9,677 datasets using YOLOv5s, YOLOv5m, and YOLOv5l algorithms. The accuracy of the deep learning model using the YOLOv5s, YOLOv5m, and YOLOv5l algorithms was 89.35%; 91.29%; and 79.43% respectively. The precision value obtained is 99.90%; 99.80%; and 99.89% respectively. While the recall value obtained was 89.43%; 91.46%; and 79.50% respectively. Based on these results indicate that the deep learning model was capable to detect rice stands with high accuracy and precision. The model can be used to detect rice in the early stages of cultivation with a web-based user interface implemented with the Python programming language on a local computer.
Keyword: deep learning, rice, stand detection, unmanned aerial vehicle, YOLOv5

Judul: Development of a Deep Learning Model to Identify Oil Palm Plants through Drone Imagery
Abstrak: Komoditas kelapa sawit merupakan salah satu keunggulan dari subsektor perkebunan Indonesia. Pengindraan jauh dapat diadaptasi pada perkebunan kelapa sawit adalah dengan melakukan upaya perhitungan jumlah pohon kelapa sawit pada suatu cakupan area tertentu. Penghitungan jumlah pohon kelapa sawit merupakan praktik perkebunan yang dapat berguna untuk inventarisasi aset biologis, estimasi jumlah produksi kelapa sawit, dan pemantauan pertumbuhan tanaman. Penelitian ini bertujuan untuk menerapkan metode YOLOv8s dan YOLOv8m untuk sistem identifikasi dan perhitungan pohon kelapa sawit secara otomatis berdasarkan tangkapan citra drone, lalu membandingkan hasilnya dengan penelitian sebelumnya yang menggunakan algoritme YOLOv3, YOLOv4, dan YOLOv5m. Dari delapan model yang didapat dari penelitian ini, diperoleh bahwa model terbaik untuk YOLOv8s adalah model dengan input size 832 dan batch 16 dengan F1-score sebesar 0,996 dan mAP50-95 sebesar 0,995. Sedangkan pada YOLOv8m adalah model dengan input size 608 dan batch 32 dengan hasil F1-score sebesar 0,996 dan mAP50-95 sebesar 0,995. Delapan model baru yang diperoleh relatif memiliki performa yang lebih baik dari penelitian sebelumnya., The palm oil commodity is one of the advantages of the Indonesian plantation sub-sector. Remote sensing that can be adapted to oil palm plantations is to make efforts to calculate the number of oil palm trees in a certain coverage area. Calculating the number of oil palm trees is a plantation practice that can be useful for inventorying biological assets, estimating the amount of oil palm, and monitoring plant growth. This study aims to apply the YOLOv8s and YOLOv8m methods to an automatic assistance and calculation system for oil palm trees based on drone image capture, and then compare the results with previous studies using the YOLOv3, YOLOv4, and YOLOv5m algorithms. The eight models which obtained from this study, it was found that the best model for YOLOv8s was the model with an input size of 832 and a batch of 16 with an F1-score of 0.996 and a mAP50-95 of 0.995. Whereas the YOLOv8m is a model with an input size of 608 and a batch of 32 with an F1-score of 0.996 and a mAP50-95 of 0.995. The eight new models obtained have relatively better performance than previous studies.
Keyword: algoritme YOLO, deep learning, kelapa sawit

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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