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: Identification of Rice Fields and Rice Growth Stages Using Drone RGB and Multispectral Imagery with an Object-Based Image Analysis Approach
Abstrak: Perkembangan teknologi penginderaan jauh secara signifikan mampu meningkatkan kemampuan dalam melakukan pemetaan atau monitoring lahan seperti sawah. Penggunaan satelit dalam pemetaan memiliki keterbatasan di antaranya akuisisi data yang memiliki resolusi spasial dan temporal yang rendah dan adanya gangguan atmosfer. Teknologi drone dapat digunakan sebagai alternatif untuk mengatasi kelemahan pada citra satelit dengan resolusi spasial yang tinggi. Pengolahan citra dengan pendekatan OBIA mampu mengatasi kelemahan analisis berbasis piksel yang ada, karena selain mempertimbangkan nilai piksel, juga mempertimbangkan ukuran dan bentuk objek di dalam citra. Kombinasi OBIA dengan algoritma machine learning seperti Random Forest (RF) dan Support Vector Machine (SVM) pada beberapa penelitian mampu menghasilkan akurasi klasifikasi yang baik. Penelitian ini bertujuan membandingkan hasil klasifikasi algoritma RF dan SVM menggunakan data citra drone RGB dan Multispektral dalam mengidentifikasi petakan dan fase sawah dengan pendekatan OBIA. Hasil analisis menunjukkan klasifikasi dengan pendekatan berbasis objek cukup efektif dalam memetakan petakan sawah. Klasifikasi SVM memiliki perfoma yang lebih unggul dalam melakukan identifikasi petakan dan fase sawah dibandingkan dengan RF. Adapun citra drone RGB dan multispektral, keduanya bekerja cukup baik pada kedua algoritma yang digunakan. Kemampuan drone multispektral lebih konsisten dan lebih baik pada petak 7B, sedangkan citra drone RGB kemampuannya lebih baik pada petak 4B., The development of remote sensing technology can significantly increase the ability to map or monitor land such as rice fields. The use of satellites in mapping has limitations including data acquisition which has low spatial and temporal resolution and the presence of atmospheric disturbances. Drone technology can be used as an alternative to overcome the weaknesses in satellite imagery with high spatial resolution. Image processing with the OBIA approach can overcome the weaknesses of the existing pixel-based analysis, because in addition to considering the pixel value, it also considers the size and shape of the objects in the image. The combination of OBIA with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) has in several studies been able to produce good classification accuracy. This study aims to compare the classification results of the RF and SVM algorithms using RGB and Multispectral drone image data in identifying rice fields and rice growth stages using the OBIA approach. The results of the analysis show that classification with an object-based approach is quite effective in mapping rice fields. The SVM classification has superior performance in identifying rice fields and rice growth stages compared to RF. As for the RGB and multispectral drone images, both works quite well on the two algorithms used. The ability of multispectral drones is more consistent and better on plot 7B, while the ability of drone RGB imagery is better on plot 4B.
Keyword: multispectral, random forest, RGB, rice field, support vector machine

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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