Leisa Armstrong

Zernike moments and genetic algorithm: Tutorial and application

Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic intelligent system has been built with PNN classifier. The feature extracted viz ZM and Geometric features were further subjected to GA to bring the best combinatorial features for optimal accuracy. The algebraic structure of our novel fitness function enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a classification accuracy of 90.05%. A demonstration of affine properties of ZM are comprehensively stated and explained. In summary, the ZM enabled the classifier to have improved accuracy of 91% as compared with Geometric features with 89% accuracy.

Rice crop yield prediction in India using support vector machines

Food production in India is largely dependent on cereal crops including rice, wheat and various pulses. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Variability in seasonal climate conditions can have detrimental effect, with incidents of drought reducing production. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. This paper presents the review on use of such machine learning technique for Indian rice cropping areas. This paper discusses the experimental results obtained by applying SMO classifier using the WEKA tool on the dataset of 27 districts of Maharashtra state, India. The dataset considered for the rice crop yield prediction was sourced from publicly available Indian Government records. The parameters considered for the study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.

An automated approach to detect human ovarian tissues using type P63 counter stained histopathology digitized color images

The human ovary contains a fixed number of reproductive tissues at birth. This number decreases with age which leads to complications in conceiving. As part of the medical consultation process and to know the actual condition of a female ovary, manual microscopic routine examination is carried out by pathology experts. Laboratory expert manual microscopic analysis is time consuming and prone to errors. To minimize labor cost and time associated with manual analysis an ultrasound technique is commonly used. This ultrasound technique can only identify larger and more mature tissues rather than small ovarian tissues. To analyze small ovarian reproductive tissues accurately a fully automated approach is proposed in this paper to assist pathology experts. The proposed method processes digitized color histopathology images acquired from biopsy slide and identifies ovarian reproductive tissues automatically. The studys experimental results indicate excellent performance in terms of accuracy.

An Automated Detection Process to Detect Ovarian Tissues Using Type P63 Digitized Color Images

Dramatic improvements have been made in the field of digital image processing especially for biomedical image analysis over the past decade. With the availability of modern digital scanners, histopathology slides can be easily stored in digitized color image format. Therefore, histopathology digitized images have become a popular data source for both computer vision and machine learning techniques. There are several computer aided algorithms currently available to assist pathology experts to carry out their routine examination for detecting various tissues such as ovarian cancer cells and ovarian reproductive tissues. Automated detection of ovarian reproductive tissues is one of the important diagnosis interests for pathologists these days. One of the popular diagnosis preferences to identify ovarian tissues is ultrasound scanner. However, due to different shape, size and color, identification of ovarian tissues is a challenging task for ultrasound scanners as it process gray scale images. At present, pathological microscopic manual analysis is considered the best laboratory analysis practice for ovarian tissue cells although it is time-consuming, laborious and prone to errors. An alternate option would be to analyze these ovarian tissues automatically using color digitized images acquired from microscopic slides. In this paper a fully automated detection approach for color digitized image acquired from microscopic slides is presented and analyzed. The proposed method was found to be faster in comparison to other approaches. The approach also is beneficial as experts will not need to tune processing parameters for new batches of images. Experimental results from an analysis of the proposed approach using batch processing of a large number of images indicated high degree of accuracy and performance compared to the manual microscopic analysis.

Effective ICTs in agricultural value chains to improve food security: An international perspective

This paper examines the grains value chain in agriculture, and identifies the importance in developing strategies which could better secure food production. The study highlights examples of successful integration of ICTs in agricultural supply and value chains. The development of strategies to integrate these ICTs into the supply chain will be proposed. It will be argued that the use of high powered computing for data mining and other technologies such as sensor networks, mobile communications, and GPS technologies can revolutionize the efficiency of these supply chains and therefore improve the food security. The study carried out a situational analysis of agricultural resources using standard internet search engines and applying data mining techniques in order to demonstrate how such technologies can be used to show difference in value chains across different situations. An assessment of the study found that the results from the grain-industry dataset support the similar supply chain grouping reported for other research studies. These groupings reflect the more-developed food-industry supply chains and may not capture all the interactions in less-developed supply chains. For example, when several of the food production processes are carried out by one food-producer, the activities will be more difficult to identify.

Use of Information and Communication Technology (ICT) Tools by Rural Farmers in Ratnagiri District of Maharastra, India

This paper discusses the impact of information and communication technology (ICT) on the access for rural farmers from the Ratnagiri district to agricultural information. A study was undertaken in which more than one hundred randomly selected farmers completed a structured questionnaire to gather information at household level of the use of ICT. Interviews were also conducted with key stakeholders, service providers and government officials. Findings from the study indicated that farmers were most interested in obtaining market price information. Examination of the relationship between use of ICT tools and co factors such as age, qualifications and income indicated that only income was a determining factor of using ICT tools.

PredictingRice crop yield using Bayesian networks

Rice crop production plays a vital role in food security of India, contributing more than 40% to overall crop production. High crop production is dependent on suitable climatic conditions. Detrimental seasonal climate conditions such as low rainfall or temperature extremes can dramatically reduce crop yield. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in important decision making in terms of agronomy and crop choice. This paper reports on the use of Bayesian Networks to predict rice crop yield for Maharashtra state, India. For this study, 27 districts of Maharashtra were selected on the basis of available data from publicly available Indian Government records with various climate and crop parameters selected. The parameters selected for the study were precipitation, minimum temperature, average temperature, maximum temperature, reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. The dataset was processed using the WEKA tool. The classifiers used in the study were BayesNet and NaiveBayes. The experimental results showed that the performance of BayesNet was much better compared with NaiveBayes for the dataset.

Rice crop yield prediction using artificial neural networks

Rice crop production contributes to the food security of India, more than 40% to overall crop production. Its production is reliant on favorable climatic conditions. Variability from season to season is detrimental to the farmers income and livelihoods. Improving the ability of farmers to predict crop productivity in under different climatic scenarios, can assist farmers and other stakeholders in making important decisions in terms of agronomy and crop choice. This study aimed to use neural networks to predict rice production yield and investigate the factors affecting the rice crop yield for various districts of Maharashtra state in India. Data were sourced from publicly available Indian Governments records for 27 districts of Maharashtra state, India. The parameters considered for the present study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. The dataset was processed using WEKA tool. A Multilayer Perceptron Neural Network was developed. Cross validation method was used to validate the data. The results showed the accuracy of 97.5% with a sensitivity of 96.3 and specificity of 98.1. Further, mean absolute error, root mean squared error, relative absolute error and root relative squared error were calculated for the present study. The study dataset was also executed using Knowledge Flow of the WEKA tool. The performance of the classifier is visually summarized using ROC curve.

Type P63 Non-counter Stained Digitized Color Images Performs Better Identification than Other Stains for Ovarian Tissue Analysis

In comparison to existing electronic scanners biopsy slides analysis using microscopes is considered as most viable approach in the pathology laboratory to perform routine examination and to determine the actual condition of human ovarian reproductive tissues. An expert needs longer processing time during manual microscopic analysis and there is inconsistent result among experts. A suitable computerized approach may be a more viable option to overcome the issues associated with manual approach. In this paper different types of images acquired from different biopsy slides were incorporated for a comprehensive review and analysis purpose. A new modified approach has been presented in this study which can reduce processing time and indicates improved accuracy. Comparative results indicate that type P63 non-counter stained performs better identification result.

A comprehensive analysis and review: Automated ovarian tissue detection using type P63 pathology color images

Microscopic biopsy slides are used in the pathology laboratory by experts for general routine examination process to analyze various types of tissues. The manual microscopic analysis using biopsy slides is considered as a most viable approach but requires substantial amount of time and has observation variation issues among experts especially for smaller types tissue analysis. Available existing imaging modalities especially ultrasound scanner is a common device to analyze various types of tissues and is mainly suitable for larger tissue analysis. Computerized automated approach could be a more viable option as smaller tissues can be analyzed with less effort and in a short period of time with an acceptable accuracy rate. In this paper a complete review has been carried out on existing available approaches and a new modified approach has been presented for type P63 histopathology color images using three different magnifications which indicates improved accuracy rate.