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"neural network" AND plant

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...
September 5, 2018: 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
Benjamin M Bader, Konstantin Jügelt, Luise Schultz, Olaf H-U Schroeder
Background: Details of the extraction and purification procedure can have a profound impact on the composition of plant-derived extracts, and thus on their efficacy and safety. So far, studies with head-to-head comparison of the pharmacology of Ginkgo extracts rendered by different procedures have been rare. Objective: The objective of this study was to explore whether Ginkgo biloba L. (Ginkgoaceae) leaf extract medications of various sources protect against amyloid beta toxicity on primary mouse cortex neurons growing on microelectrode arrays, and whether the effects differ between different Ginkgo extracts...
2018: Frontiers in Pharmacology
Wenlong Ma, Zhixu Qiu, Jie Song, Jiajia Li, Qian Cheng, Jingjing Zhai, Chuang Ma
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted based on genome-wide markers of genotypes. In this study, we present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypes when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional genotypic data...
November 2018: Planta
Salvador Gutiérrez, Juan Fernández-Novales, Maria P Diago, Javier Tardaguila
Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine ( Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h...
2018: Frontiers in Plant Science
Sarah Taghavi Namin, Mohammad Esmaeilzadeh, Mohammad Najafi, Tim B Brown, Justin O Borevitz
Background: High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants...
2018: Plant Methods
Shiwen Xie, Yongfang Xie, Tingwen Huang, Weihua Gui, Chunhua Yang
In the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process. This paper proposes a coordinated optimization strategy for setting the descent gradient of OFIC. First, an optimal setting module is established to determine the initial set-points of the descent gradient...
May 21, 2018: IEEE Transactions on Cybernetics
Jing Wei Tan, Siow-Wee Chang, Sameem Binti Abdul Kareem, Hwa Jen Yap, Kien-Thai Yong
Automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbour (k-NN), Naïve-Bayes (NB) and CNN...
June 19, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Shuang Zhang, Yiting Dong, Yuncheng Ouyang, Zhao Yin, Kaixiang Peng
This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known...
March 8, 2018: IEEE Transactions on Neural Networks and Learning Systems
Shichao Jin, Yanjun Su, Shang Gao, Fangfang Wu, Tianyu Hu, Jin Liu, Wenkai Li, Dingchang Wang, Shaojiang Chen, Yuanxi Jiang, Shuxin Pang, Qinghua Guo
The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge. Deep learning, a state-of-the-art machine learning method, has shown high performance in object detection, classification, and segmentation...
2018: Frontiers in Plant Science
Taewon Moon, Tae In Ahn, Jung Eek Son
In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers ( Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014...
2018: Frontiers in Plant Science
Noemí López-González, Santiago Andrés-Sánchez, Blanca M Rojas-Andrés, M Montserrat Martínez-Ortega
This study exhaustively explores leaf features seeking diagnostic characters to aid the classification (assigning cases to groups, i.e. populations to taxa) in a polyploid plant-species complex. A challenging case study was selected: Veronica subsection Pentasepalae, a taxonomically intricate group. The "divide and conquer" approach was implemented-that is, a difficult primary dataset was split into more manageable subsets. Three techniques were explored: two data-mining tools (artificial neural networks and decision trees) and one unsupervised discriminant analysis...
2018: PloS One
Abhirup Mookherjee, Ramalingam Dineshkumar, Nithya N Kutty, Tarun Agarwal, Ramkrishna Sen, Adinpunya Mitra, Tapas Kumar Maiti, Mrinal Kumar Maiti
Quorum sensing, the microbial communication system, is gaining importance as a therapeutic target against pathogens. The two key reasons for the rising demand of quorum sensing (QS) inhibitory molecules are low selective pressure to develop resistance by pathogens and possibility of more species-specific effects. Due to complex interactions in a unique niche of live plant tissues, endophytes, as a survival mechanism, potentially produce various bioactive compounds such as QS inhibitors. We report the isolation of an endophytic fungus Kwoniella sp...
June 22, 2018: Applied Microbiology and Biotechnology
Paulo J S Gonçalves, Letícia M Estevinho, Ana Paula Pereira, João M C Sousa, Ofélia Anjos
The aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To obtain the predominant plant genus of pollen (was the output variable), based on physicochemical composition (were the input variables of the predictive model), prediction models were learned from data. For the inverse case study, input/output variables were swapped...
November 30, 2018: Food Chemistry
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