Study of Various Techniques for Medicinal Plant Identification

Ayurveda, the Indian ancient medicinal system, has gained importance because of its effectiveness in treating diseases. Medicinal plants are used in Ayurvedic medicines since ancient times. It is necessary to classify these plants so that it would be easy to select the right plant for the medicinal preparation or to study more about its characteristics. Identification is the pre-condition of classification of medicinal plant. In this paper, we have reviewed Image processing Near-Infrared Spectroscopy (NIRS), taxonomic key repository, neural network and DeoxyriboNucleic Acid (DNA) barcoding. The study shows that image processing is leading domain in identification of medicinal plant. The results are improved when multiple methods are used together in a sequence to identify a medicinal plant. Apart from that none of these methods are using geographical information to identify medicinal plants and we can use geographical Information System (GIS) information to improve its accuracy further

Selection of crop varieties and yield prediction based on phenotype applying deep learning

In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield

Hardware-software co-design of AES on FPGA

This paper presents a compact hardware-software co-design of Advanced Encryption Standard (AES) on the field programmable gate arrays (FPGA) designed for low-cost embedded systems. The design uses MicroBlaze, a soft-core processor from Xilinx. The computationally intensive operations of the AES are implemented in hardware for better speed. The sub-byte calculation is designed with the help of the processor carrying out the calculations using hardware blocks implemented using FPGA. By incorporating the processor in the AES design, the total number of slices required to implement the AES algorithm on FPGA is proved to be reduced. The entire AES system design is validated using 460 slices in Spartan-3E XC3S500E, which is one of the low-cost FPGA

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page

Sentiment Analysis of Assamese Text Reviews: Supervised Machine Learning Approach with Combined n-gram and TF-IDF Feature

Sentiment analysis (SA) is a challenging application of natural language processing (NLP) in various Indian languages. However, there is limited research on sentiment categorization in Assamese texts. This paper investigates sentiment categorization on Assamese textual data using a dataset created by translating Bengali resources into Assamese using Google Translator. The study employs multiple supervised ML methods, including Decision Tree, K-nearest neighbour, Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, combined with n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction methods. The experimental results show that Multinomial Naive Bayes and Support Vector Machine have over 80% accuracy in analyzing sentiments in Assamese texts, while the Unigram model performs better than higher-order n-gram models in both datasets. The proposed model is shown to be an effective tool for sentiment classification in domain-independent Assamese text data

Smart Home Solutions Using Wi-Fi-based Hardware

Home automation technology has been increasingly important in our lives, since it offers numerous advantages, e.g., greater comfort, safety, security and energy efficiency. A smart home automation system usually includes a central computer with deployed home automation software and several distributed sensors and actuators. Wired connections between a central computer and sensor/actuator nodes are already well established, however, wireless solutions are an emerging trend. This work addresses smart home automation solutions that are based on wireless Wi-Fi network. Such solutions enable an upgrade of an existing house into a smart house without modifications of hardware installations. The article includes an overview of related works in this research field, and a case study of cost effective home automation solution that is based on open source home automation software and wireless, custom developed, Wi-Fi based hardware

Amateur radio sensing technique using a combination of energy detection and waveform classification

A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects

A Novel Mining Approach for Automatic Disease Detection in Sugarcane Plant Using Thresholding Method

Disease infection of agricultural products affects the agriculturists and human health and also degrades the quality and quantity of products. The tradition approach for detecting a disease is time consuming and very costly. The plant diseases are detected by automatic detection techniques that reduce a large work of continuous monitoring and observation in big farms by farmers or experts. The proposed algorithms detect the variety of diseases infected in sugarcane plants. The images are captured by digital camera. The noises in digital image are removed by low pass filter. This paper presents image segmentation using thresholding method which is used for automatic disease detection of sugarcane plants. SIFT method is applied for detecting and describing the local features of the plant species. The features such as colors, size shape and texture of surface is extracted by using GLCM feature extractor. The abnormal images are classified by using SVM classifier. In sugarcane plant, the diseases are detected automatically and yields 99% accuracy rate than existing techniques