Efficient Deep Learning model for de-husked Areca nut classification

Areca nut is a widely used agricultural product in India and even over the globe. Areca nut, a fruit of   areca palm (Areca catechu) is grown widely in the Asia-Pacific region.. Areca nut segregation is of prime importance in the areca nut industry. The quality segregation of peeled/de-husked nuts requires skilled workers. This process of manual segregation is time-consuming and can lead to erroneous classification. Recent deep learning (DL) advances have improved the performance in multi-class problems. The present  work presents the classification of de-husked areca nut among five classes using an efficient deep learning customized Convolutional Neural Network (CNN) and the results of this model were compared with the standard AlexNet architecture. The new CNN model was customized to obtain classification accuracy higher than the existing ones. A dataset of 300 nuts (60 per class) was created using a specially designed instrumentation setup. The areca nut images were then pre-processed and fed to these models to learn the features of the areca nut from different classes. The confusion matrix and Area Under the Curve - Receiver Operating Characteristics (AUC- ROC) were employed to assess the results of these models and cross-validated with 5 and 10-fold. The experimental results show that the CNN outperformed the AlexNet model with an average accuracy of 97.33% and 98.34%, F1 score of 97.48%, and 98.45% for 5 and 10 folds, respectively.  

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods

Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties

This research is aimed at evaluating the texture and shape features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of texture and shape features and neural network was done to classify four Paddy (rice) grains, namely, Karjat-6(K6), Ratnagiri-2(R2), Ratnagiri-4(R4), and Ratnagiri-24(R24). Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and used as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature from the features for accurate classification was identified. The shape feature set outperformed the texture feature set in almost all the instances of classification

Optimization of areca catechu seed oil extracted by supercritical carbon dioxide (scco2) using principle component analysis (PCA)

A study on the optimization of supercritical carbon dioxide (SCCO2) on areca catechu seed oil was carried out at temperature and pressure ranging from 50 °C to 80 °C and 20 MPa to 30 MPa respectively. Three grams of ground areca catechu seeds with particle size of 177.5 µm and moisture content of 10.95% were used in the SCCO2 extraction process that was carried out for 60 minutes. Fourier transform infra-red spectroscopy (FTIR) analysis was applied to determine the profile of SCCO2 extracts in each condition. A chemometrics method of Principal Component Analysis (PCA) was applied to the FTIR spectra to optimize the condition for catechin extraction. The scores and plot show that, the highest quality of catechin was detected in the sample extracted at 70 °C and 30 MPa. Whereas the lowest quality of the catechin was detected in the sample extracted at 50 °C and 20 MPa. The highest concentration of catechin was found in the sample extracted at 50 °C and 20 MPa. Overall, the sample extracted at 70 °C and 30 MPa possessed the highest quality of catechin in low concentration. The areca catechu oil was further analyzed for the physicochemical properties. The Acid value of areca catechu is 18.7 ± 0.06 mg NaOH / g oil or 9.397 % free fatty acid as oleic acid and iodine value is 69.54 ± 0.57 g I2/100g. Whereas peroxide and saponification values are 6.5 mequiv.O2/kg and 173.91 ± 0.64 mg KOH/g respectively. A thermogravimetric analysis was used to study the pyrolysis temperature range. The decomposition of the sample started at 160 °C and ended at 470 °C. Whereas the Gas Chromatography Mass Spectrometer (GCMS) analysis detected 12 fatty acids in areca catechu oil sample. There are 8 saturated fatty acids consisting of caprylic acid (C8:0), capric acid (C10:0), lauric acid (C12:0), myristic acid (C14:0), palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0) and behenic acid (C22:0) and 4 unsaturated fatty acid consisting of palmitoleic acid (C16:1), oleic acid (C18:1), linoleic acid(C18:2) and a- linolenic acid (C18:3)

Food science applications and international trends of artificial neural networks

Recently, research has been focusing increasingly on the system of artificial neural networks, and its results are used in many places by industrial practices. The success of these networks lies in their ability to recognize the complex relationships and patterns in data, as well as to predict unknown samples, thus enabling value and category predictions with high certainty. Artificial neural networks are very efficient tools for modeling non-linear trends within data. In many cases, they perform well where traditional statistical tools provide unsatisfactory results or unable to solve a given research problem. In our work, the operation principle and structure (topol-ogy) of artificial neural networks are summarized, as well as the classification and application possibilities of the networks. The latest food science applications are presented separately, based on the usage type (prediction, classification, optimiza-tion). Results show that artificial neural networks possess many beneficial properties, making them especially suitable for solving food science tasks

The impact of betel quid chewing during pregnancy on pregnancy outcomes in Bhutan

Betel (areca) nut is the fourth most widely used psychoactive substance globally, accounting for 10-20% of the world’s population. Its most basic form is betel ‘quid’ which consists of betel leaf, betel nut (the main psychoactive ingredient) and slaked lime. Evidence that betel quid and betel nut alone are associated with oral cancer has been established. Background: While there is a substantial body of evidence on the impact of health-risk behaviours including smoking and drinking alcohol on adverse pregnancy outcomes, studies on the impact of betel quid chewing on pregnancy outcomes are sparse and heterogeneous. Although several studies report the negative impact of betel quid chewing on pregnancy outcomes, the evidence is inconclusive. One of the challenges in understanding the impact of betel quid is to distinguish the impact of betel quid chewing from the impact of smoking. Bhutan, where low prevalence of smoking and high prevalence of betel-quid chewing are reported, provides a natural experimental environment for taking a close look at the impact of betel quid chewing alone. As a part of the global agenda to address preterm births (PTB) as a public health priority and in order to provide evidence to inform efforts to reduce neonatal morbidity and mortality in Bhutan, this study explores the impact of betel quid chewing on birth outcomes and its importance in relation to other risk factors. Methods: This study used a multi-centre case-control design. A case was defined as a mother of a singleton live born infant whose gestational age is less than 37 completed weeks and/or an infant whose birth weight is less than 2500 g. A control was defined as a mother of singleton live born term babies whose birth weight was more than 2500g and gestational age was greater than 37 weeks. Information was collected using a semi-structured questionnaire from February 2015 to the beginning of March 2016 at the three referral hospitals in Bhutan. Study participants were recruited by a trained interviewer during their post-delivery stay before discharge from each hospital. A statistical approach and a causal directed acyclic graph (DAG) approach were used for building logistic regression models. Results: Of the 669 study participants, 55% of the case mothers and 52% of the control mothers chewed betel quid during pregnancy. About 22% of cases and 22% of controls used commercial betel products during pregnancy. In total, 60% of the case mothers and 57% of the control mothers chewed either betel quid or packaged betel products during pregnancy. Neither the statistical approach nor DAG approach provided clear evidence of an association between betel quid use and low birth weight (LBW) or PTB. The adjusted odds ratio (aOR) of term LBW was 1.07 (95% CI: 8 0.54-2.13, p=0.845) in the statistical approach while the aOR of term LBW was 1.30 (95% CI: 0.74-2.27, p=0.439) in the DAG approach. Using the DAG approach, the aOR of PTB in association with betel quid chewing during pregnancy was 1.20 (95% CI: 0.72-2.00, p=0.614). When the total number of betel nuts consumed during the last three months of pregnancy was used as an exposure variable, the aOR for mothers who consumed more than one nut per day was 1.39 for term LBW (95%:0.52-3.68, p=0.514) and the aOR of PTB was 0.66 (95% CI: 0.27-1.66, p=0.383) compared to non-chewers. For a secondary outcome, the data suggest betel quid chewing is associated with increased odds of anaemia (aOR 2.09, 95% CI 1.27-3.43, p=0.004). Using the DAG approach, tobacco and alcohol use during pregnancy, low gestational weight gain, and urinary tract infection showed a clear association with term LBW and PTB. Conclusion: In the present study, the results provide no clear evidence of an association between term LBW or PTB and betel quid chewing during pregnancy. For a secondary outcome, the data suggest betel quid chewing is associated with increased odds of anaemia. The present study provides rich baseline data for mothers and established a cohort of cases and controls, which could be followed up to understand the short- and long-term effects of LBW and PTB and may help design effective interventions

Classification and severity prediction of maize leaf diseases using Deep Learning CNN approaches

No key words availableMaize (zea mays) is the staple food of Southern Africa and most of the African regions. This staple food has been threatened by a lot of diseases in terms of its yield and existence. Within this domain, it is important for researchers to develop technologies that will ensure its average yield by classifying or predicting such diseases at an early stage. The prediction, and to some degree classifying, of such diseases, with much reference to Southern Africa staple food (Maize), will result in a reduction of hunger and increased affordability among families. Reference is made to the three diseases which are Common Rust (CR), Grey Leaf Spot (GLS) and Northern Corn Leaf Blight (NCLB) (this study will mainly focus on these). With increasing drought conditions prevailing across Southern Africa and by extension across Africa, it is very vital that necessary mitigation measures are put in place to prevent additional loss of crop yield through diseases. This study introduces the development of Deep Learning (DL) Convolutional Neural Networks (CNNs) (note that in this thesis deep learning or convolution neural network or the combination of both will be used interchangeably to mean one thing) in order to classify the disease types and predict the severity of such diseases. The study focuses primarily on the CNNs, which are one of the tools that can be used for classifying images of various maize leaf diseases and in the severity prediction of Common Rust (CR) and Northern Corn Leaf Blight (NCLB). In essence the objectives of this study are: i. To create and test a CNN model that can classify various types of maize leaf diseases. ii. To set up and test a CNN model that can predict the severities of a maize leaf disease known as the maize CR. The model is to be a hybrid model because fuzzy logic rules are intended to be used with a CNN model. iii. To build and test a CNN model that can predict the severities of a maize leaf disease known as the NCLB by analysing lesion colour and sporulation patterns. This study follows a quantitative study of designing and developing CNN algorithms that will classify and predict the severities of maize leaf diseases. For instance, in Chapter 3 of this study, the CNN model for classifying various types of maize leaf diseases was set up on a Java Neuroph GUI (general user interface) framework. The CNN in this chapter achieved an average validation accuracy of 92.85% and accuracies of 87% to 99. 9% on separate class tests. In Chapter 4, the CNN model for the prediction of CR severities was based on fuzzy rules and thresholding methods. It achieved a validation accuracy of 95.63% and an accuracy 89% when tested on separate images of CR to make severity predictions among 4 classes of CR with various stages of the disease’ severities. Finally, in Chapter 5, the CNN that was set up to predict the severities of NCLB achieved 100% of validation accuracy in classification of the two NCLB severity stages. The model also passed the robustness test that was set up to test its ability of classifying the two NCLB stages as both stages were trained on images that had a cigar-shaped like lesions. The three objectives of this study are met in three separate chapters based on published journal papers. Finally, the research objectives were evaluated against the results obtained in these three separate chapters to summarize key research contributions made in this work.College of Engineering, Science and TechnologyPh. D. (Science, Engineering and Technology

Oncology of different populations and the ethical impact of the HeLa cell case and physician assisted suicide on patient autonomy

An in depth analysis of different types of cancers in various populations such as pediatrics, adults of all ages, and babies that are still in the womb. Discusses the importance of funding research and finding new methods of treatment. This explains the ethical impact of the Henrietta Lacks case and Physician Assisted Suicide (PAS) on patient autonomy and the importance of allowing patients to decide if they want to start or even continue cancer treatments

Glycaemic effects of betel nut chewing in Type 2 Diabetes Mellitus

This research sought to access the effects of betel nut chewing on glycaemic control amongst patients with type 2 diabetes mellitus in Papua New Guinea. Whilst there were no observable acute effects of betel nut chewing on capillary blood glucose levels, a negative association was observed between betel nut use and glycaemic control in patients with long-term diabetes. Furthermore, betel nut consumption had a negative association with body mass index (BMI) and waist circumference