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"neural network" AND plant

Dor Oppenheim, Guy Shani, Orly Erlich, Leah Tsror
Many plant diseases have distinct visual symptoms which can be used to identify and classify them correctly. This paper presents a potato disease classification algorithm which leverages these distinct appearances and the recent advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network training it to classify the tubers into five classes, namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes and diseases, was acquired, classified, and labeled manually by experts...
December 13, 2018: Phytopathology
Mirjana Perić, Katarina Rajković, Aleksandra Milić Lemić, Rade Živković, Valentina Arsić Arsenijević
OBJECTIVE: The upward trend in using plant materials introduced essential oils (EOs) as a valuable, novel, bioactive antifungal agent and as an alternative to standard treatment protocol of denture stomatitis caused by Candida species. Therefore, the aim was to evaluate the antifungal activity of different EOs and to present the response surface methodology (RSM) and artificial neural network (ANN) as possible tools for optimizing and predicting EOs antifungal activity. METHODS: Minimum inhibitory concentration (MIC) and Minimum fungicidal concentration (MFC) of the EOs against 3 species Candida spp...
December 1, 2018: Archives of Oral Biology
C P Devatha, N Pavithra
Triclosan (TCS) is a well-known emerging contaminant got wide use in daily use products of domestic purpose, which provides the way to enter the ecological cycle, and is preferably detected in sewage treatment plants. In this study, TCS degrading bacteria (TDB) was isolated and identified from a wastewater treatment plant at the National Institute of Technology-Karnataka, Surathkal (NITK), India. The isolate was reported as Pseudomonas strain by performing 16S RNA Sequencing using BLAST analysis. Bacterial growth depends upon several environmental factors...
December 1, 2018: Journal of Environmental Management
Yishan Guo, Zhewei Xu, Chenghang Zheng, Jian Shu, Hong Dong, Yongxin Zhang, Weiguo Weng, Xiang Gao
Sulfur dioxide (SO2 ) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as main method for SO2 removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network was developed to forecast SO2 emissions...
November 30, 2018: Journal of the Air & Waste Management Association
David Zimmer, Kevin Schneider, Frederik Sommer, Michael Schroda, Timo Mühlhaus
Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays...
2018: Frontiers in Plant Science
Qi Zhao, Qian Mao, Zheng Zhao, Tongyi Dou, Zhiguo Wang, Xiaoyu Cui, Yuanning Liu, Xiaoya Fan
BACKGROUND: An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. RESULTS: Here, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs...
November 26, 2018: BMC Genomics
Xiangzhou Wang, Lin Liu, Xiaohui Du, Jing Zhang, Juanxiu Liu, Guangming Ni, Ruqian Hao, Yong Liu
Unlike urine or blood samples with a single background, human fecal samples contain large amounts of food debris, amorphous particles, and undigested plant cells. It is difficult to segment such impurities when mixed with leukocytes. Cell degradation results in ambiguous nuclei, incompleteness of the cell membrane, and a changeable cell morphology, which are difficult to recognize. Aiming at the segmentation problem, a threshold segmentation method combining an inscribed circle and circumscribed circle is proposed to effectively remove the adhesion impurities with a segmentation accuracy reaching 97...
November 1, 2018: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Md Mehedi Hasan, Joshua P Chopin, Hamid Laga, Stanley J Miklavcic
Background: Field phenotyping by remote sensing has received increased interest in recent years with the possibility of achieving high-throughput analysis of crop fields. Along with the various technological developments, the application of machine learning methods for image analysis has enhanced the potential for quantitative assessment of a multitude of crop traits. For wheat breeding purposes, assessing the production of wheat spikes, as the grain-bearing organ, is a useful proxy measure of grain production...
2018: Plant Methods
Shuai Sui, C L Philip Chen, Shaocheng Tong
This paper solves the finite-time switching control issue for the nonstrict-feedback nonlinear switched systems. The controlled plants contain immeasurable states, arbitrarily switchings, and the unknown functions which are constructed with the whole states. Neural network is used to simulate the uncertain systems and a filter-based state observer is designed to estimate the immeasurable states in this paper, respectively. Based on the backstepping recursive technique and the common Lyapunov function method, a finite-time switching control method is presented...
November 14, 2018: IEEE Transactions on Neural Networks and Learning Systems
Shuang Cai, Ahmet Palazoglu, Laibin Zhang, Jinqiu Hu
Industrial alarm systems play an essential role for the safe management of process operations. With the increase in automation and instrumentation of modern process plants, the number of alarms that the operators manage has also increased significantly. The operators are expected to make critical decisions in the presence of flooding alarms, poorly configured and maintained alarms and many nuisance alarms. In this environment, if the incoming alarms can be correctly predicted before they actually occur, the operators may have a chance to address and possibly avoid abnormal behaviors by taking corrective actions in time...
October 30, 2018: ISA Transactions
H Al-Saddik, A Laybros, J C Simon, F Cointault
Flavescence Dorée (FD) is a contagious and incurable grapevine disease that can be perceived on leaves. In order to contain its spread, the regulations obligate winegrowers to control each plant and to remove the suspected ones. Nevertheless, this monitoring is performed during the harvest and mobilizes many people during a strategic period for viticulture. To solve this problem, we aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution. If embedded on a UAV, the tool can provide disease outbreaks locations in a geographical information system allowing localized and direct treatment of infected vines...
2019: Methods in Molecular Biology
Chao Ni, Dongyi Wang, Yang Tao
Spectroscopy is a powerful non-destructive quantization tool. In this paper, the technology is used to predict the nitrogen content of Masson pine seedling leaves. Masson pine is widely planted in China, and its nitrogen content is an important index for evaluating the vigour of seedings. To establish a better prediction model, an improved 1D convolutional neural network architecture, named the variable weighted convolutional neural network (VWCNN), is proposed in this research. The new model can automatically force the network attention onto the important spectrum wavelengths and is able to improve the generalization ability of the basic 1D-CNN model...
February 15, 2019: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
Bin Yang, Yuehui Chen, Wei Zhang, Jiaguo Lv, Wenzheng Bao, De-Shuang Huang
Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT)...
October 15, 2018: International Journal of Molecular Sciences
Byunghyun Kim, Soojin Cho
At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures. However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet...
October 14, 2018: Sensors
Yanjun Zhang, Gang Tao, Mou Chen, Wei Lin, Zhengqiang Zhang
This paper studies the relative degrees of discrete-time neural network systems in a general noncanonical form, and develops a new feedback control scheme for such systems, based on implicit function theory and feedback linearization. After time-advance operation on output of such systems, the output dynamics nonlinearly depends on the control input. To address this issue, we use implicit function theory to define the relative degrees, and to establish a normal form. Then, an implicit function equation solution-based control scheme and an iterative solution-based control scheme are proposed, which ensure not only the closed-loop stability but also the output tracking for the controlled plant...
September 26, 2018: IEEE Transactions on Cybernetics
Yongeun Park, Minjeong Kim, Yakov Pachepsky, Seoung-Hwa Choi, Jeong-Goo Cho, Junho Jeon, Kyung Hwa Cho
Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and ) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City...
September 2018: Journal of Environmental Quality
Augusto Cesar Fonseca Saraiva, André Mesquita, Terezinha Ferreira de Oliveira, Rachel Ann Hauser-Davis
The aim of the present study consisted in evaluating the effects of CO2 enrichment on the growth and biometal/nutrient content and accumulation in Senna reticulata germinated under two different carbon dioxide concentrations: atmospheric (360 mg L-1 ) and elevated (720 mg L-1 ). Biometal/nutrient determinations were performed on three different plant portions (leaflets, stem and root) using flame atomic absorption spectrometry. In general, the biometal and nutrient stoichiometries in roots were increased, probably due to reduced transpiration, and consequent biometal accumulation...
December 2018: Journal of Trace Elements in Medicine and Biology
Isham Alzoubi, Mahmoud R Delavar, Farhad Mirzaei, Babak Nadjar Arrabi
Background: Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines require considerable amount of energy, it delivers a suitable surface slope with minimal deterioration of the soil and damage to plants and other organisms in the soil. Notwithstanding, researchers during recent years have tried to reduce fossil fuel consumption and its deleterious side effects. The aim of this work was to determine best linear model using artificial neural network (ANN), imperialist competitive algorithm and ANN and regression and adaptive neural fuzzy inference system (ANFIS) in order to predict the environmental indicators for land leveling...
June 2018: Journal of Environmental Health Science & Engineering
Majid Bagheri, Khalid Al-Jabery, Donald C Wunsch, Joel G Burken
Uptake of contaminants from the groundwater is one pathway of interest, and efforts have been made to relate root exposure to transloation throughout the plant, termed the transpiration stream concentration factor (TSCF). This work utilized machine learning techniques and statistcal analysis to improve the understanding of plant uptake and translocation of emerging contaminants. Neural network (NN) was used to develop a reliable model for predicting TSCF using physicochemical properties of compounds. Fuzzy logic was as a technique to examine the simultaneous impact of properties on TSCF, and interactions between compound properties...
February 15, 2019: Science of the Total Environment
Alvaro F Fuentes, Sook Yoon, Jaesu Lee, Dong Sun Park
A fundamental problem that confronts deep neural networks is the requirement of a large amount of data for a system to be efficient in complex applications. Promising results of this problem are made possible through the use of techniques such as data augmentation or transfer learning of pre-trained models in large datasets. But the problem still persists when the application provides limited or unbalanced data. In addition, the number of false positives resulting from training a deep model significantly cause a negative impact on the performance of the system...
2018: Frontiers in Plant Science
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