KLASIFIKASI JENIS KOPI INDONESIA MENGGUNAKAN DEEP LEARNING

Indonesia adalah salah satu negara produsen dan eksportir kopi terbesar di dunia. Perkembangan bisnis kopi mengalami kemajuan yang cukup pesat, mulai dari tingkat para petani, pemasok, café kopi, hingga ke konsumen biasa. Disamping meningkatnya kemajuan industri kopi di indonesia masih terdapat banyak masalah yang menimbulkan kerugian secara material dan rasa ketidakpuasan baik bagi pelaku usaha maupun para pecinta kopi. Masalah yang muncul diakibatkan karena industry ini masih banyak dijalankan dengan menggunakan sistem kepercayaan antar pihak yang berkaitan. Sulit untuk system sederhana membedakan anatara satu varian kopi dengan varian lainnya. Diperlukannya sebuah system berbasis teknologi informasi yang dapat membantu mengenali dan memastikan secara langsung bahwa kopi yang dibutuhkan dan dinikmati sudah sesuai dengan apa yang diinginkan. Sistem informasi yang akan dibangun dapat mengklasifikasi jenis kopi berdasarkan gambar. Pengenalan pola citra tersebut menggunakan Deep Learning. Melatih algoritma Deep Learning untuk mendeteksi jenis kopi secara akurat membutuhkan jumlah gambar yang banyak untuk data pelatihan. Metode pengenalan menggunakan Convolutional Neural Network yang dapat digunakan untuk mengenali objek pada sebuah gambar dan sering digunakan untuk klasifikasi data berupa image. Metode CNN saat ini trend digunakan untuk masalah klasifikasi gambar dikarenakan tingkat akurasinya yang sangat tinggi. CNN akan mengklasifikasi pada setiap gambar yang disiapkan sebagai data latih untuk pengenalan. Data dikumpulkan dengan cara pengambilan gambar biji kopi menggunakan kamera. Kumpulan data ini berisi 4 jenis kopi asal Indonesia (Garut, Gayo, Kerinci, Temanggung) dengan jumlah 617 gambar biji kopi. Setelah dilakukan proses pengujian, system dapat mengenali objek dengan tingkat akurasi sebesar 70,68%

Artificial intelligence and sensory assessment of hair assembly features: a combined approach

An explorative comparison of the sensitivity of human perception with that of classification algorithms for machine learning when applied to human hair tresses

Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda

Automatic Detection and Calculation of Palm Oil Fresh Fruit Bunches using Faster R-CNN

Indonesia is one of the countries with the largest industry of crude palm oil (CPO) in the world. During 2013-2017, the growth of the area of oil palm plantations in Indonesia decreased -0.52%, the decline is expected not to affect the amount of CPO production. One of the things that affect CPO production is the primary raw material availability of palm oil fresh fruit bunches (FFB). Raw material requirements can be predicted by several forecasting methods, but the methods only predict the raw material requirements FFB, not the availability. The development of deep learning eases humans in doing things. Deep learning can be used to calculate FFB automatically using the faster R-CNN algorithm. This study presented a system of automatic detection and calculation of FFB. The evaluation is carried out by comparing 4 network architectures; resnet inception V2, inception V2, resnet 50, and resnet 101. The results of this study indicate success in calculating FFB. The success is indicated by the results of evaluating the four network models with the average F1 scores above 80%

Artificial Intelligence in hair research: a proof-of-concept study on evaluating hair assembly features

The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms. Machine learning was applied to a dataset of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments), t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front-image based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms. The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair. This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis, and highlighted the challenges imposed by the presentation mode

MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by how experienced manga artists draw manga, MangaGAN generates the geometric features of manga face by a designed GAN model and delicately translates each facial region into the manga domain by a tailored multi-GANs architecture. For training MangaGAN, we construct a new dataset collected from a popular manga work, containing manga facial features, landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, we further propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces which preserve both the facial similarity and a popular manga style, and outperforms other related state-of-the-art methods.Comment: 17 page

Hair detection, segmentation, and hairstyle classification in the wild

Abstract Hair highly characterises human appearance. Hair detection in images is useful for many applications, such as face and gender recognition, video surveillance, and hair modelling. We tackle the problem of hair analysis (detection, segmentation, and hairstyle classification) from unconstrained view by relying only on textures, without a-priori information on head shape and location, nor using body-part classifiers. We first build a hair probability map by classifying overlapping patches described by features extracted from a CNN, using Random Forest. Then modelling hair (resp. non-hair) from high (resp. low) probability regions, we segment at pixel level uncertain areas by using LTP features and SVM. For the experiments we extend Figaro, an image database for hair detection to Figaro1k, a new version with more than 1000 manually annotated images. Achieved segmentation accuracy (around 90%) is superior to known state-of-the-art. Images are eventually classified into hairstyle classes: straight, wavy, curly, kinky, braids, dreadlocks, and short