Search results for: plant classification

Authors: J. K. Alhassan, S. A. Lawal

Abstract:

This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Sungho Kim, Dae Shik Kim, Jong Min Lee

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Fluid catalytic cracking (FCC) process is one of the most important process in modern refinery industry. This paper focuses on the fluid catalytic cracking (FCC) process. As the FCC process is difficult to model well, due to its non linearities and various interactions between its process variables, rigorous process modeling of whole FCC plant is demanded for control and plant-wide optimization of the plant. In this study, a process design for the FCC plant includes riser reactor, main fractionator, and gas processing unit was developed. A reactor model was described based on four-lumped kinetic scheme. Main fractionator, gas processing unit and other process units are designed to simulate real plant data, using a process flow sheet simulator, Aspen PLUS. The custom reactor model was integrated with the process flow sheet simulator to develop an integrated process model.

Keywords: fluid catalytic cracking, simulation, plant data, process design

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm

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Authors: Atakan Efe Akpınar, Zeynep Demir

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Seed priming technologies are frequently used nowadays to increase the germination potential and stress tolerance of seeds. These treatments might be beneficial for native species as well as crops. Different priming treatments can be used depending on the type of plant, the morphology, and the physiology of the seed. Moreover, these may be various physical, chemical, and/or biological treatments. Aiming to improve studies about seed priming, ideas need to be brought into this technological sector related to the agri-seed industry. This study addresses the question of whether seed priming with plant extracts can improve seed vigour and germination performance. By investigating the effects of plant extract priming on various vigour parameters, the research aims to provide insights into the potential benefits of this treatment method. Thus, seed priming was carried out using some plant extracts. Firstly, some plant extracts prepared from plant leaves, roots, or fruit parts were obtained for use in priming treatments. Then, seeds of Common zinnia (Zinnia elegans) were kept in solutions containing plant extracts at 20°C for 48 hours. Seeds without any treatment were evaluated as the control group. At the end of priming applications, seeds are dried superficially at 25°C. Seeds of Common zinnia (Zinnia elegans) were analyzed for vigour (normal germination rate, germination time, germination index etc.). In the future, seed priming applications can expand to multidisciplinary research combining with digital, bioinformatic and molecular tools.

Keywords: seed priming, plant extracts, germination, biology

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Yavuz Demir, Mucip Genisel, Hulya Turk, Turgay Sisman, Serkan Erdal

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In this study, the changes in contents of inorganic elements in the aquatic plant (Potamogeton natans) as a reflection of the impact of chemical nature pollution in a cement factory region (CFR) was evaluated. For this purpose, P, S, K, Ca, Fe, Cl, Mn, Cu, Zn, Mo, Ni, Si, Al, and Cd concentrations were measured in the aquatic plant (Potamogeton natans) taken from a CFR. As a control, aquatic plant was collected at a distance of 2000 m from the outer zone of the cement factory. Inorganic element compositions were measured by energy dispersive X-ray fluorescence spectrometry (EDXRF). Three aquatic plant exhibited similar changes in contents of microelements and macroelements in their leaves. P, S, K, Cl, Ca, and Mo contents in plant grown in the CFR were reduced significantly compared to control plant, whereas their contents of Al, Mn, Fe, Ni, Cu, Zn and Cd were very high. According to these findings, it is possible that aquatic plant (Potamogeton natans) inhabiting in the vicinity of cement factory sustains the deficiency of important essential elements like P, S, K, Ca, and Mo and greatly accumulate heavy metals like Al, Mn, Fe, Ni, Cu, Zn, and Cd. In addition, results of water analysis showed that heavy metal content such as Cu, Pb, Zn, Co, and Al of water taken from CFR was remarkably high than that of outer zone of CFR. These findings with relation to changes in inorganic composition can contribute to be elucidated of effect mechanism on growth and development of aquatic plant (Potamogeton natans) of pollution resulted from cement factories.

Keywords: aquatic plant, cement factory, heavy metal pollution, inorganic element, Potamogeton natans

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Authors: Mohamad Sakizadeh, Fatemeh Mehrabi Sharafabadi, Hadi Ghorbani

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Concentrations of Cr and Ni in 97 plant samples (belonged to eight different plant species) and the associated soil groups were considered in this study. The amounts of Ni in soil groups fluctuated between 26.8 and 36.8 mgkg⁻¹ whereas the related levels of chromium ranged from 67.7 to 94.3mgkg⁻¹. The index of geoaccumulation indicated that 87 percents of the studied soils for chromium and 98.8 percents for nickel are located in uncontaminated zone. The results of Mann-Whitney U-test proved that agricultural practices have not significantly influenced the values of Ni and Cr. In addition, tillage had also little impact on the Ni and Cr transfer in the surface soil. Ni showed higher accumulation and soil-to-plant transfer factor compared with that of chromium in the studied plants. There was a high similarity between the accumulation pattern of Cr and Fe in most of the plant species.

Keywords: bioconcentration factor, chromium, geoaccumulation index, nickel

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Authors: Sujing Wang, Song Wang, Jian Zhang, Qiang Xu

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Flaring emissions during abnormal operating conditions such as plant start-ups, shut-downs, and upsets in chemical process industries (CPI) are usually significant. Flare minimization can help to save raw material and energy for CPI plants, and to improve local environmental sustainability. In this paper, a systematic methodology based on plant-wide dynamic simulation is presented for CPI plant flare minimizations under abnormal operating conditions. Since off-specification emission sources are inevitable during abnormal operating conditions, to significantly reduce flaring emission in a CPI plant, they must be either recycled to the upstream process for online reuse, or stored somewhere temporarily for future reprocessing, when the CPI plant manufacturing returns to stable operation. Thus, the off-spec products could be reused instead of being flared. This can be achieved through the identification of viable design and operational strategies during normal and abnormal operations through plant-wide dynamic scheduling, simulation, and optimization. The proposed study includes three stages of simulation works: (i) developing and validating a steady-state model of a CPI plant; (ii) transiting the obtained steady-state plant model to the dynamic modeling environment; and refining and validating the plant dynamic model; and (iii) developing flare minimization strategies for abnormal operating conditions of a CPI plant via a validated plant-wide dynamic model. This cost-effective methodology has two main merits: (i) employing large-scale dynamic modeling and simulations for industrial flare minimization, which involves various unit models for modeling hundreds of CPI plant facilities; (ii) dealing with critical abnormal operating conditions of CPI plants such as plant start-up and shut-down. Two virtual case studies on flare minimizations for start-up operation (over 50% of emission savings) and shut-down operation (over 70% of emission savings) of an ethylene plant have been employed to demonstrate the efficacy of the proposed study.

Keywords: flare minimization, large-scale modeling and simulation, plant shut-down, plant start-up

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Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Kehinde T. Kareem

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The need to increase the production of cucumber has led to the use of inorganic fertilizers. This chemical affects the ecological balance of nature by increasing the nitrogen and phosphorus contents of the soil. Surface runoffs into rivers and streams cause eutrophication which affects aquatic organisms as well as the consumers of aquatic animals. Therefore, this study was carried out in the screenhouse to investigate the use of a plant growth-promoting fungus; Trichoderma longibrachiatum for the growth promotion of conventional and in-vitro propagated Ashley and Marketmoor cucumber. Before planting of cucumber, spore suspension (108 cfu/ml) of Trichoderma longibrachiatum grown on Potato dextrose agar (PDA) was inoculated into the soil. Fruits were evaluated for the presence of Trichoderma longibrachiatum using a species-specific primer. Results revealed that the highest significant plant height produced by in-vitro propagated Ashley was 19 cm while the highest plant height of in-vitro propagated Marketmoor was 19.67 cm. The yield of the conventional propagated Ashley cucumber showed that the number of fruit/plant obtained from T. longibrachiatum-fertilized plants were significantly more than those of the control. The in-vitro Ashely had 7 fruits/plant while the control produced 4 fruits/plant. In-vitro Marketmoor had ten fruits/plant, and the control had a value of 4 fruits/plant. There were no traces of Trichoderma longibrachiatum genes in the harvested cucumber fruits. Therefore, the use of Trichoderma longibrachiatum as a plant growth-promoter is safe for human health as well as the environment.

Keywords: biofertilizer, cucumber, genes, growth-promoter, in-vitro, propagation

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Authors: Michelle Sánchez de León Brajkovich, Nuria Martí Audi

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The envelope is one of the most important elements when one analyzes the operation of the building in terms of sustainability. Taking this into consideration, this research focuses on setting a classification system of the envelopes opaque systems, crossing the knowledge and parameters of construction systems with requirements in terms of sustainability that they may have, to have a better understanding of how these systems work with respect to their sustainable contribution to the building. Therefore, this paper evaluates the importance of the envelope design on the building sustainability. It analyses the parameters that make the construction systems behave differently in terms of sustainability. At the same time it explains the classification process generated from this analysis that results in a classification where all opaque vertical envelope construction systems enter.

Keywords: sustainable, exterior walls, envelope, facades, construction systems, energy efficiency

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Yongjian Gu

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Ocean thermal energy is one of the ocean energy sources. It is a renewable, sustainable, and green energy source. Ocean thermal energy conversion (OTEC) applies the ocean temperature gradient between the warmer surface seawater and the cooler deep seawater to run a heat engine and produce a useful power output. Unfortunately, the ocean temperature gradient is not big. Even in the tropical and equatorial regions, the surface water temperature can only reach up to 28oC and the deep water temperature can be as low as 4oC. The thermal efficiency of the OTEC plants, therefore, is low. In order to improve the plant thermal efficiency by using the limited ocean temperature gradient, some OTEC plants use the method of adding more equipment for better heat recovery, such as heat exchangers, pumps, etc. Obviously, the method will increase the plant's complexity and cost. The more important impact of the method is the additional equipment needs to consume power too, which may have an adverse effect on the plant net power output, in turn, the plant thermal efficiency. In the paper, the author first describes varied OTEC plants and the practice of using the method of adding more equipment for improving the plant's thermal efficiency. Then the author proposes a parameter, plant back works ratio ϕ, for measuring if the added equipment is appropriate for the plant thermal efficiency improvement. Finally, in the paper, the author presents examples to illustrate the application of the back work ratio ϕ as a key parameter in the OTEC plant design and operation.

Keywords: ocean thermal energy, ocean thermal energy conversion (OTEC), OTEC plant, plant back work ratio ϕ

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Authors: Rifa’ah Mahmudah Bulu’

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MRSA is an important nosocomial pathogen in the world. Therefore, the prevention and effort to control MRSA is still very important to conduct. One of the preventions of MRSA, which have been reported by several studies, is Cefriaxone and Ethanol Extract of Ant Plant. This research is an experimental test to determine the potency of MRSA’s anti-bacterial with Cefriaxone (30 μg) and Ethanol Extract of Ant Plant (13 mg/ml) based on inhibition zone on LAB (Lempeng Agar Biasa). The size of inhibition zone that is formed on Cefriaxone is adjusted with CSLI criteria, which ≥ 21 mm of inhibition zone is called sensitive; ≤13 mm is called resistance and between 14-20 mm is called intermediate. This research is conducted three times. Comparative test between Cefriaxone and Ethanol Extract of Ant Plant is analyzed by Maan Whitney’s statistic method. The Result of Cefriaxone anti-bacterial potency shows the variety of inhibition zone. Cefriaxone forms approximately 16,5-20 mm with average 18,22mm of inhibition zone that make Cefriaxone’s criteria to MRSA’s inhibition is intermediate. Anti-bacterial potency of Ethanol Extract of Ant Plant is about 0,5-2 mm with average 1,17 mm of inhibition zone that prove MRSA is sensitive to Ant Plant. The conclusion of this research shows that Cefriaxone is intermediate to MRSA’s inhibition, while MRSA is sensitive to Ethanol Extract of Ant Plant, which at the end; it creates different potency of anti-bacterial between Cefriaxone and Ethanol Extract of Ant Plant.

Keywords: MRSA, cefriaxone, ant plant, CSLI, mann whitney

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Authors: Bharatendra Rai

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The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Farzad Alaeimoghadam, Farnaz Alaeimoghadam

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Plants are always being threatened by biotic and abiotic elements in their abode. Although they have inherited mechanisms to defend themselves, sometimes due to overpowering of their enemies or weakening of themselves, they just suffer from those elements. Human, as to help plants defend themselves, have developed several methods among which application of terpenes via plant biotechnology is promising. Terpenes are the most frequent and diverse secondary metabolites in plants. In these plants, terpenes are involved in different protective aspects. In this field, by utilizing biotechnological approaches on them, a delicate, precise, and an economic intervention will be achieved. In this review, first, the importance of terpenes as guardians in plants, which include their allelopathy effect, a call for alliances, and a mitigation impact on abiotic stresses will be pointed out. Second, problems concerning terpenes application in plant biotechnology comprising: damage to cell, undesirable terpene production and undesirable concentration and proportion of terpenes will be discussed. At the end, the approaches in plant biotechnology of terpenes including tampering with terpene gene sequences, compartmentalization, and localization and utilization of membrane transporters will be expressed. It is concluded with some useful notions concerning the topic.

Keywords: plant biotechnology, plant protection, terpenes, terpenoids

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Fernando Duarte

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The work aims to evaluate the use of classical image processing methodologies towards skin tone classification in dermatological images. The skin tone is an important attribute when considering several factor for skin cancer diagnosis. Currently, there is a lack of clear methodologies to classify the skin tone based only on the dermatological image. In this work, a recent released dataset with the label for skin tone was used as reference for the evaluation of classical methodologies for segmentation and adjustment of color space for classification of skin tone in dermatological images. It was noticed that even though the classical methodologies can work fine for segmentation and color adjustment, classifying the skin tone without proper control of the aquisition of the sample images ended being very unreliable.

Keywords: segmentation, classification, color space, skin tone, Fitzpatrick

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Authors: Obiora E. Anisiji, Jeremiah L. Chukwuneke, Chinonso H. Achebe, Paul C. Okolie

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This work developed a mathematical model of a biogas plant from a mechanistic point of view, for urban area clean energy requirement. It aimed at integrating thermodynamics; which deals with the direction in which a process occurs and Biochemical kinetics; which gives the understanding of the rates of biochemical reaction. The mathematical formulation of the proposed gas plant follows the fundamental principles of thermodynamics, and further analysis were accomplished to develop an algorithm for evaluating the plant performance preferably in terms of daily production capacity. In addition, the capacity of the plant is equally estimated for a given cycle of operation and presented in time histories. A nominal 1500m3 biogas plant was studied characteristically and its performance efficiency evaluated. It was observed that the rate of biogas production is essentially a function of enthalpy ratio, the reactor temperature, pH, substrate concentration, rate of degradation of the biomass, and the accumulation of matter in the system due to bacteria growth. The results of this study conform to a very large extent with reported empirical data of some existing plant and further model validations were conducted in line with classical records found in literature.

Keywords: anaerobic digestion, biogas plant, biogas production, bio-reactor, energy, fermentation, rate of production, temperature, therm

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Authors: Khadri Sihem, Abbaci Nafissa, Zerari Labiba

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In the context of the search for new bioactive natural products, we were interested in evaluating some antibacterial properties of two plant extracts: total phenols and flavonoids of Algerian medicinal plant. Our study occurs in two axes: The first concerns the extraction of phenolic compounds and flavonoids with methanol by liquid-liquid extraction, followed by quantification of the levels of these compounds in the end the analysis of the chemical composition of extracts. In the second axis, we studied the antibacterial power of the studied plant extracts.

Keywords: antibacterial activity, flavonoids, medicinal plants, polyphenols

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Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

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Authors: Gauravi Agarkar, Mahendra Rai

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Fungi are closely associated with the plants in various types of interactions such as mycorrhizal, parasitic or endophytic. Some of these interactions are beneficial and a few are harmful to the host plants. It has been suggested that these plant-associated fungi are able to change their lifestyle abd this means endophyte may become parasite or vice versa. This phenomenon may have profound effect on plant-fungal interactions and various ecological niches. Therefore, it is necessary to identify the factors that trigger the change in fungal lifestyle and understand whether these different lifestyles are interconnected at some points either by physiological, biochemical or molecular routes. This review summarizes the factors affecting plant fungal interactions and discusses the possible mechanisms for lifestyles switching of fungi based on available experimental evidences. Research should be boosted in this direction to fetch more advantages in future and to avoid the severe consequences in agriculture and other related fields.

Keywords: endophytic, lifestyle switching, mycorrhizal, parasitic, plant-fungal interactions

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Authors: Shweta Shriwas, Sumeet Dwivedi, Raghvendra Dubey

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Plumeria pudica Linn. Family Apocynaceae commonly known as Nag Chmapa is grown wildly in many parts of India. The plant is medium size shrub, grown up to height of 5-10 feet, evergreen with white flowers. In traditional system of medicine, the plant is widely used in the treatment of worms, infection, inflammation, etc. So, far no any systematic and documented study was done to revealed quality control parameters and pharmacological aspect of the selected plant species, therefore, the attempt was made in present investigation to reveal the same. The parameters such as Ash value, FOM, LOD, SI, etc. were studied using various coarsely dried plant materials of the species. Analgesic, anti-inflammatory, anthelmentic and anti-microbial activity of various extract was investigated and reported in present work.

Keywords: Plumeria pudica, quality control, pharmacology, parameters

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Authors: W. Promprom, W. Chatan

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Leguminosae is a large plant family and its members are important for local people utilization in the Northeast of Thailand. This research aimed to survey medicinal plants in this family in Kantharalak Community forest. The plant collection and exploration were made from October 2017 to September 2018. Folk medicinal uses were studied by interviewing villagers and folk medicine healers living around the community forest by asking about local names, using parts, preparation and properties. The results showed that 65 species belonging to 40 genera were found. Among these, 30 species were medicinal plant. The most used plant parts were leaf. Decoction and drinking were mostly preparation method and administration mode used. All medicinal plants could be categorized into 17 diseases/symptoms. Most plant (56.66%) were used for fever. The voucher specimens were deposited in Department of Biology, Faculty of Science, Mahasarakham University, Thailand. Therefore, the data from this study might be widely used by the local area and further scientific study.

Keywords: ethnobotany, ethnophamacology, medicinal plant, taxonomy, utilization

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Authors: Begüm Terzi, Özlem Ateş Sönmezoğlu, Kerime Özkay, Ahmet Yıldırım

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In plant biotechnology, electrophoresis is used to detect nucleic acids. Ethidium bromide (EtBr) is used as an intercalator dye to stain DNA in agarose gel electrophoresis, but this dye is mutagenic and carcinogenic. In this study, a visible, reliable and organic Ruthenium-based dye (N-719) for staining plant DNA in comparison to EtBr. When prestaining and post-staining for gel electrophoresis, N-719 stained both DNA and PCR product bands with the same clarity as EtBr. The organic dye N-719 stained DNA bands as sensitively and as clearly as EtBr. The organic dye was found to have staining activity suitable for the identification of DNA.Consequently, N-719 organic dye can be used to stain and visualize DNA during gel electrophoresis as alternatives to EtBr in plant biotechnology studies.

Keywords: agarose gel, DNA staining, organic dye, N-719

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Authors: Rahim Ada, Ahmet Tamkoç

Abstract:

The research was conducted in the “Randomized Complete Block Design” with three replications in research field of Agricultural Faculty, Selcuk University, Konya, Turkey. In study, a total of 9 Cephalaria syriaca promised lines (9, 37, 38, 42, Beyaz 4, 5 Beyaz, 13 Beyaz, 27 Beyaz, Başaklar 2), which were taken from Sivas population, and 1 population were evaluated in two growing seasons (2012-13 and 2013-14). According to the results, the highest plant height, first branch height, first head height, number of branches per plant, number of head per plant, head diameter,1000 seed weight, seed yield, oil content and oil yield were obtained respectively from Başaklar 2 (68.37 cm), Başaklar 2 (37.80 cm), Başaklar 2 (54.83 cm), 37 (7.73 number/plant), 42 (18.03 number/plant), 9 (10.30 mm), Başaklar 2 (19.33 g), 27 Beyaz (1254.2 kg ha-1), Başaklar 2 (28.77%), and 27 Beyaz (357.9 kg ha-1).

Keywords: Cephalaria syriaca, yield, oil, population

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Authors: Abdelkhalek Bakkari

Abstract:

Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

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