DETEKSI KEMATANGAN TANDAN BUAH SEGAR (TBS) KELAPA SAWIT BERDASARKAN KOMPOSISI WARNA MENGGUNAKAN DEEP LEARNING

Classification of oil palm fresh fruit bunch (FFB) based on maturity is very important for estimating oil content. Traditional methods using human vision to observe color changes during ripening and counting the number of fruits that fall from FFB are not effective. Research for neural architectures to design new network bases and improve them resulted in a set of models called EfficientNet. The most important function is the optimizer. This function repeatedly increases the parameters to reduce loss. In this study, the EfficientNetB0 and B1 models were developed to detect oil palm maturity into 6 classes, Raw, Ripe, Overripe, Underripe, abnormal, and empty bunch using optimizer RMSprop and SGD. From the research results, obtained the highest accuracy using the RMSprop optimizer of 0.9955 using the EfficientNetB0 model and 0.9949 using the EfficientNetB1 model. While using the SGD optimizer, the accuracy achieved is 0.918 using the EfficientNetB0 model and 0.9079 using the EfficientNetB1 mode

An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection

Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. These processes are slow, expensive and subject to evaluators' bias and fatigue. Recent advancements in deep learning can help automate this process. A method was developed to automatically count the number of sweet cherry fruits in a camera's field of view in real time using YOLOv3. A system capable of analyzing the image data for other traits such as size and color was also developed using Python. The YOLO model obtained close to 99% accuracy in object detection and counting of cherries and 90% on the Intersection over Union metric for object localization when extracting size and colour information. The model surpasses human performance and offers a significant improvement compared to manual counting.Comment: Published in 25th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'21

Facial expression recognition via a jointly-learned dual-branch network

Human emotion recognition depends on facial expressions, and essentially on the extraction of relevant features. Accurate feature extraction is generally difficult due to the influence of external interference factors and the mislabelling of some datasets, such as the Fer2013 dataset. Deep learning approaches permit an automatic and intelligent feature extraction based on the input database. But, in the case of poor database distribution or insufficient diversity of database samples, extracted features will be negatively affected. Furthermore, one of the main challenges for efficient facial feature extraction and accurate facial expression recognition is the facial expression datasets, which are usually considerably small compared to other image datasets. To solve these problems, this paper proposes a new approach based on a dual-branch convolutional neural network for facial expression recognition, which is formed by three modules: The two first ones ensure features engineering stage by two branches, and features fusion and classification are performed by the third one. In the first branch, an improved convolutional part of the VGG network is used to benefit from its known robustness, the transfer learning technique with the EfficientNet network is applied in the second branch, to improve the quality of limited training samples in datasets. Finally, and in order to improve the recognition performance, a classification decision will be made based on the fusion of both branches’ feature maps. Based on the experimental results obtained on the Fer2013 and CK+ datasets, the proposed approach shows its superiority compared to several state-of-the-art results as well as using one model at a time. Those results are very competitive, especially for the CK+ dataset, for which the proposed dual branch model reaches an accuracy of 99.32, while for the FER-2013 dataset, the VGG-inspired CNN obtains an accuracy of 67.70, which is considered an acceptable accuracy, given the difficulty of the images of this dataset

PENGGUNAAN METODE DEEP LEARNING EFFICIENTNETB1 UNTUK MENGENALI SAMPAH ORGANIK DAN SAMPAH ANORGANIK

ABSTRAK Indonesia selalu muncul masalah lingkungan yang berkaitan dengan sampah. Pada tahun 2020 jumlah sampah di Indonesia mencapai 67,8 juta ton. Salah satu metode pengelolaan sampah yaitu metode Organic Solid Waste. OSW memiliki kekurangan diantaranya adalah efisiensi yang buruk, biaya yang tinggi, dan memiliki potensi terhadap ancaman kesehatan manusia yang melakukan pemilahan sampah. Untuk membantu hal tersebut perlu dibangun sebuah model yang bisa melakukan klasifikasi jenis sampah secara cepat, dan efisien, salah satu metode yang bisa digunakan yaitu Convolutional Neural Network (CNN). CNN memiliki arsitektur yang menjadi state-of-the-art yaitu EfficientNet-B1 merupakan metode yang memiliki efisiensi, dan kinerja yang lebih baik dengan meningkatkan model menggunakan tuning hyperparameter. Tunning hyperparameter yang digunakan yaitu learning rate, optimizer, dan fungsi aktivasi. Akurasi tertinggi pada eksperimen 5 dengan tuning hyperparameter, menggunakan split data 90:10, fungsi aktivasi Rectified Linear Unit, optimizer Adam, dan learning rate 0.01 menghasilkan nilai F1-Score 99,68%. Pengujian aplikasi web terhadap model terbaik yang telah di-deployment berhasil melakukan prediksi terhadap data yang berbeda. Kata Kunci : Convolutional, EfficientNet-B1, F1-Score, Hyperparameter Tuning, Sampah Organi