scholarly journals Mineral Rock Classification Using Convolutional Neural Network

2021 ◽  
Author(s):  
Shanmuk Srinivas Amiripalli ◽  
Grandhi Nageshwara Rao ◽  
Jahnavi Behara ◽  
K Sanjay Krishna ◽  
Mathurthi pavan venkat durga ram
Keyword(s):  
Neural Network ◽  
Chemical Composition ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Image Recognition ◽  
Negative Impact ◽  
Rock Classification ◽  
Rock Types ◽  
Image Dataset ◽  
Neural Network Algorithm

The main aim of the research is to build a model that can effectively predict the type of mineral rocks. Rocks can be predicted by observing it is colour, shape and chemical composition. On-site technicians need to apply different techniques on rock sample in order to predict rock type. Technicians need to apply different techniques on rock samples, so it is a time-consuming process, and sometimes the predictions may be accurate, and sometimes predictions may be false. When predictions are false, it might show a negative impact in several ways for workers and organization as well. We considered an image dataset of rock types, namely Biotite, Bornite, Chrysocolla, Malachite, Muscovite, Pyrite, and Quartz. We applied CNN (Convolutional Neural Network) Algorithm to get a better prediction of different mineral rocks. Nowadays, CNN is mainly used for image classification and image recognition tasks.

scholarly journals Literature Study of Convolutional Neural Network Algorithm for Batik Classification

2021 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Nardianti Dewi Girsang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Algorithm Analysis ◽  
Image Size ◽  
Literature Study ◽  
Aesthetic Value ◽  
Network Algorithm ◽  
Neural Network Algorithm

Batik is a hereditary cultural heritage that has high aesthetic value and deep philosophy. Currently, Indonesian batik has various types of different motifs and patterns, which are spread in Indonesia with their names and meanings. Batik classification uses Convolutional Neural Network as a pattern recognition method, especially batik image classification. The method used is a literature study, looking at studies from several journals regarding the Convolutional Neural Network Algorithm in Classification and providing conclusions about the usefulness of the algorithm. Analysis This literature study analyzes each journal from previous research related to the Convolutional Neural Network Algorithm in classifying Batik. The results of the analysis, conducted a discussion to better know the characteristics and application of Convolutional Neural Network in the classification of Batik. After discussing, this analysis ends with conclusions about the Convolutional Neural Network algorithm in classifying Batik. Based on previous studies, it can be seen that the convolution neural network can work well for image classification with large datasets. By evaluating the method that has been described by considering the architecture and the level of accuracy, namely getting an accuracy level of 100% with an image size of 128 x 128 and regarding the classification of batik, it shows that image size, image quality, image patterns affect the batik classification process.

scholarly journals Machine Learning for Detection of Palm Oil Leaf Disease Visually using Convolutional Neural Network Algorithm

2021 ◽  
Vol 4 (2) ◽  
pp. 286-293
Author(s):  
Asrianda Asrianda ◽  
Hafizh Al Kautsar Aidilof ◽  
Yoga Pangestu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Palm Oil ◽  
Leaf Disease ◽  
Neural Network Algorithm

Artificial intelligence (AI) merupakan bidang ilmu pengetahuan yang saat ini menjadi isu yang menarik dan masih diteliti secara luas. Salah satu cabang dari pengembangan AI adalah computer vision yang di dalamnya terdapat topik pembahasan image classification dan object detection. Machine learning dapat dimanfaatkan di dalam bidang computer vision untuk melakukan object detection dan image classification, yaitu dengan menggunakan algoritma Convolutional Neural Network (CNN). CNN banyak digunakan pada penelitian terdahulu karena akurasinya yang tinggi. Pada penelitian ini, CNN digunakan untuk mendeteksi jenis penyakit daun tanaman kelapa sawit, dengan dataset sebanyak 60 gambar, dimana 50 diantaranya merupakan daun dengan 5 jenis penyakit berbeda, yaitu Curvularia sp, Cochliobolus carbonus, Capnodium sp, Drecshlera, dan defisiensi unsur hara. Sedangkan 10 sisanya merupakan gambar daun sehat. Hasilnya, CNN dapat mendeteksi penyakit daun kelapa sawit dengan akurasi yang dihasilkan mencapai 99%.

scholarly journals The Optimization of Transfer Learning Convolutional Neural Network Model with PCA and t-SNE Algorithms for Classification and Recognition of CIFAR-10 Image

2020 ◽  
Vol 4 (4) ◽  
pp. 717-722
Author(s):  
Rastri Prathivi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Neural Network Model ◽  
Transfer Of Learning ◽  
Computation Time ◽  
Image Dataset ◽  
Pca Algorithm ◽  
Image Pattern

The low accuracy when performing the image classification process is a problem that often occurs. The image classification process requires the completeness of the features of the image which form an informative image pattern so that information from the image can be displayed. The purpose of this study is to classify images in the CIFAR-10 image dataset using the CNN method. Initially the CNN method gave an accuracy of 79.4% but had a long computation time of 12 hours with 10,000 iterations. The optimization process for the CNN method is carried out by combining the CNN method, the PCA algorithm and the t-SNE algorithm. The algorithm is used to reduce the length of the image matrix in the initial transfer of learning without reducing the information in the image so that the classification process can be done correctly. The final result obtained from the optimization has an accuracy of 90.5%. With an optimization rate of 11%. The resulting time is more efficient, namely 3 hours for the feature transfer-value process and 6 minutes for the testing process with 10,000 iterations.

scholarly journals Weeds Detection in Agricultural Fields using Convolutional Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 292-296
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Weed Control ◽  
Classification System ◽  
Agricultural Fields ◽  
Use Of Technology ◽  
Neural Network Algorithm

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

scholarly journals Algorithm and Technology Application of Image Recognition Based on Artificial Intelligence

2021 ◽  
Vol 2136 (1) ◽  
pp. 012062
Author(s):  
Xuebin Zhu ◽  
Zhoulin Wang ◽  
Peiwen Lin ◽  
Ziqian Ma ◽  
Zhenghong Yu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Big Data ◽  
Social Construction ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Current Application ◽  
Technology Application ◽  
Artificial Intelligence Technology ◽  
Neural Network Algorithm

Abstract In the development of social construction, the concept of artificial intelligence technology has been widely used in all fields, among which image recognition as a representative, not only changed the traditional way of image recognition, but also solved the new problems emerging in the development of the era of big data. Therefore, on the basis of understanding the current common artificial intelligence image recognition technology content, according to the current application of artificial intelligence technology, this paper analyzes how to carry out image recognition based on convolutional neural network algorithm.

scholarly journals Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation

2020 ◽  
Vol 4 (1) ◽  
pp. 45-51
Author(s):  
Ari Peryanto ◽  
Anton Yudhana ◽  
Rusydi Umar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Image Recognition ◽  
Cross Validation ◽  
Recognition System ◽  
Multi Layer Perceptron ◽  
Average Accuracy ◽  
Python Programming ◽  
Fold Cross Validation

Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.

scholarly journals AUTOMATED WEED REMOVAL SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Mounashree J P ◽  
N Sanjay ◽  
Sushmitha B S ◽  
Usha B G ◽  
Anupama Shivamurthy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Weed Control ◽  
Raspberry Pi ◽  
Use Of Technology ◽  
Neural Network Algorithm ◽  
Removal System

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weed herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control system. The system consists of motors, Raspberry pi and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Deep learning is used to analyze the relevant features from the agricultural images. The dataset is trained for the classification of weed and crop. Therefore, by the use of technology, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

Sign in / Sign up

Export Citation Format

Share Document