Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour

Noni fruit is included in exported food commodities in Indonesia. The size of noni fruit, based on human vision, generally has varied shapes with distinctive textures and various patterns, so that the process of filtering fruit based on color and shape can be done in large quantities. In this study, K-Nearest Neighbor (KNN) has been implemented as a classification algorithm because it has advantages in classifying images and is resistant to noise. Noni imagery is a personal image taken from a noni garden in the morning and undergoes a background subtraction process. The imagery quality improvement technique uses the Hue Saturation Value (HSV) color feature and the Gray Level Co-Occurrence Matrix (GLCM) characteristic feature. KNN accuracy without features is lower than using HSV and GLCM features. From the experimental results, the highest accuracy was obtained using HSV-GLCM at K is 1 and d is 1, namely 95%, while the lowest accuracy was 55% using KNN only at K is 5 and d is 8.Buah mengkudu termasuk dalam komoditas pangan ekspor di Indonesia. Ukuran dari buah mengkudu, berdasarkan penglihatan manusia secara umumnya mempunyai bentuk bervariasi dengan tekstur khas dan pola beragam, sehingga proses filterisasi buah berdasarkan warna dan bentuk dapat dilakukan dalam jumlah besar. Dalam penelitian ini, K-Nearest Neighbour (KNN) telah diimplementasikan sebagai algoritma klasifikasi karena mempunyai keunggulan dalam mengklasifikasi citra dan tahan terhadap noise. Citra mengkudu merupakan citra pribadi yang dipotret dari kebun mengkudu pada pagi hari dan mengalami proses background subtraction. Teknik peningkatan kualitas citra menggunakan fitur warna Hue Saturation Value (HSV) dan fitur ciri Grey Level Co-Occurrence Matrix (GLCM). Akurasi KNN tanpa fitur lebih rendah dibanding menggunakan fitur HSV maupun GLCM. Dari hasil percobaan, telah diperoleh akurasi tertinggi menggunakan HSV-GLCM pada K adalah 1 dan d adalah 1 yaitu 95%, sedangkan akurasi terendah yaitu 55% menggunakan KNN saja pada K adalah 5 dan d adalah 8

Guava Fruit Detection and Classification Using Mask Region-Based Convolutional Neural Network

Guava has various types and each type has different nutritional content, shapes, and colors. It is often difficult for some people to recognize guava correctly with so many varieties of guava on the market. In industry, the classification and segmentation of guava fruit is the first important step in measuring the guava fruit quality. The quality inspection of guava fruit is usually still done manually by observing the size, shape, and color which is prone to mistakes due to human error. Therefore, a method was proposed to detect and classify guava fruit automatically using computer vision technology. This research implements a Mask Region-Based Convolutional Neural Network (Mask R-CNN) which is an extension of Faster R-CNN by adding a branch that is used to predict the segmentation mask in each region of interest in parallel with classification and bounding box regression. The system classifies guava fruit into each category, determines the position of each fruit, and marks the region of each fruit. These outputs can be used for further analysis such as quality inspection. The performance evaluation of guava detection and classification using the Mask R-CNN method achieves an mAR score of 88%, an mAP score of 90%, and an F1-Score of 89%. It can be concluded that the proposed method performs well in detecting and classifying guava fruit

Klasifikasi Buah Zaitun Menggunakan Convolution Neural Network

Olive fruit is a horticultural product of the oleaceae family with the genus Olea which has various types and unique features. One of a group of Olea species found in tropical and subtropical regions which make the plant fertile and abundant. The yields are very abundant in proportion to market needs. The random harvest of produce makes the selection of post-harvest products very important in classifying types of olives. So it is necessary to have a system that can classify automatically. Previous studies have been proposed to classify olives with considerable accuracy. However, the required speed takes a very long time because it uses a complex pretrained model. Therefore, this study aims to classify olives with a faster time and accuracy that is no less than before. The method to be used is Convolutional Neural Network (CNN) with its own architectural circuit. The results of this study get an accuracy of 92% with 30 epochs.Buah zaitun merupakan tanaman produk hortikultura rumpun oleaceae dengan genus Olea yang memiliki berbagai macam jenis dan fitur yang unik. Satu dari sekumpulan species Olea yang ditemukan di wilayah tropis dan subtropis yang menjadikan tanaman subur dan melimpah. Hasil panen yang sangat melimpah sebanding dengan kebutuhan pasar. Pemanenan produk secara acak membuat pemilihan produk pasca panen sangat penting dalam mengelompokkan jenis buah zaitun. Sehingga perlu adanya sistem yang dapat mengklasifikasi secara otomatis. Sebelumnya sudah ada penelitian yang diusulkan untuk mengklasifikasi buah zaitun dengan akurasi yang cukup tinggi. Namun kecepatan yang diperlukan butuh waktu yang sangat lama karena menggunakan model pretrained yang begitu kompleks. Oleh karena itu, penelitian ini bertujuan untuk melakukan klasifikasi buah zaitun dengan waktu yang lebih cepat dan akurasi yang tidak kalah dari sebelumnya. Metode yang akan digunakan adalah Convolutional Neural Network (CNN) dengan rangkaian arsitektur sendiri. Hasil dari penelitian ini mendapatkan akurasi sebesar 92% dengan 30 epoch

Deep learning based automatic multi-class wild pest monitoring approach using hybrid global and local activated features

Specialized control of pests and diseases have been a high-priority issue for agriculture industry in many countries. On account of automation and cost-effectiveness, image analytic based pest recognition systems are widely utilized in practical crops prevention applications. But due to powerless handcrafted features, current image analytic approaches achieve low accuracy and poor robustness in practical large-scale multi-class pest detection and recognition. To tackle this problem, this paper proposes a novel deep learning based automatic approach using hybrid and local activated features for pest monitoring solution. In the presented method, we exploit the global information from feature maps to build our Global activated Feature Pyramid Network (GaFPN) to extract pests’ highly discriminative features across various scales over both depth and position levels. It makes changes of depth or spatial sensitive features in pest images more visible during downsampling. Next, an improved pest localization module named Local activated Region Proposal Network (LaRPN) is proposed to find the precise pest objects’ positions by augmenting contextualized and attentional information for feature completion and enhancement in local level. The approach is evaluated on our 7-year large-scale pest dataset containing 88.6K images (16 types of pests) with 582.1K manually labelled pest objects. The experimental results show that our solution performs over 75.03% mAP in industrial circumstances, which outweighs two other state-of-the-art methods: Faster R-CNN with mAP up to 70% and FPN mAP up to 72%. Our code and dataset will be made publicly available

Aplicación de aprendizaje por refuerzo adversario en juegos de Atari

Actualmente, la inteligencia artificial y en particular el aprendizaje por refuerzo, se encuentra en todas partes, como la conducción autónoma, la robótica social y sistemas de vigilancia. El problema de tener tantos dominios de aplicación, es que también está expuesto a multitud de ataques que pueden causar grandes problemas. Por ejemplo, en un sistema de conducción autónoma controlado por aprendizaje por refuerzo, un ataque puede causar que el coche se estrelle causando catastróficas consecuencias. Por lo tanto, el estudio de la vulnerabilidad de estos sistemas frente a ataques está recibiendo cada vez más atención dentro de la comunidad científica. Por eso, este trabajo se centra, precisamente, en el estudio de la vulnerabilidad de estos sistemas. En particular, el objetivo principal es disminuir el rendimiento de una red neuronal profunda aplicada a juegos de Atari mediante dos nuevos tipos de ataque. En el primero, se tratará de identificar zonas/regiones de ataque, es decir, aquellas regiones donde atacar induce más rápidamente el fallo del sistema. El segundo se basará en aprendizaje por refuerzo. En este ataque se tratará de aprender una política de ataque identificado, por cada estado en el que se puede encontrar el sistema, si es mejor atacar o no atacar. En ambos casos, se obtendrán conclusiones sobre el momento exacto en el que atacar afecta más a la red, es decir, baja más su rendimiento, y sobre el número de ataques de cada aproximación, puesto que el objetivo es maximizar el daño mientras se minimiza el número de ataques. El primer paso para poder defenderte de un ataque, es conocer cómo se puede atacar y qué tipos de ataque puede haber. Por lo tanto, este documento sienta las bases sobre posibles sistemas de defensa futuros.Ingeniería Telemátic