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"neural network" AND plant

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https://www.readbyqxmd.com/read/30101399/a-deep-convolutional-neural-network-approach-for-predicting-phenotypes-from-genotypes
#1
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...
August 12, 2018: Planta
https://www.readbyqxmd.com/read/30090110/on-the-go-hyperspectral-imaging-under-field-conditions-and-machine-learning-for-the-classification-of-grapevine-varieties
#2
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
https://www.readbyqxmd.com/read/30087695/deep-phenotyping-deep-learning-for-temporal-phenotype-genotype-classification
#3
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
https://www.readbyqxmd.com/read/29994557/coordinated-optimization-for-the-descent-gradient-of-technical-index-in-the-iron-removal-process
#4
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
https://www.readbyqxmd.com/read/29994129/deep-learning-for-plant-species-classification-using-leaf-vein-morphometric
#5
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
https://www.readbyqxmd.com/read/29994076/adaptive-neural-control-for-robotic-manipulators-with-output-constraints-and-uncertainties
#6
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
https://www.readbyqxmd.com/read/29988466/deep-learning-individual-maize-segmentation-from-terrestrial-lidar-data-using-faster-r-cnn-and-regional-growth-algorithms
#7
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
https://www.readbyqxmd.com/read/29977249/forecasting-root-zone-electrical-conductivity-of-nutrient-solutions-in-closed-loop-soilless-cultures-via-a-recurrent-neural-network-using-environmental-and-cultivation-information
#8
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
https://www.readbyqxmd.com/read/29958275/divide-and-conquer-data-mining-tools-and-sequential-multivariate-analysis-to-search-for-diagnostic-morphological-characters-within-a-plant-polyploid-complex-veronica-subsect-pentasepalae-plantaginaceae
#9
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
https://www.readbyqxmd.com/read/29934653/quorum-sensing-inhibitory-activity-of-the-metabolome-from-endophytic-kwoniella-sp-py016-characterization-and-hybrid-model-based-optimization
#10
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
https://www.readbyqxmd.com/read/29934179/computational-intelligence-applied-to-discriminate-bee-pollen-quality-and-botanical-origin
#11
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
https://www.readbyqxmd.com/read/29894946/-1-h-nmr-metabolomics-of-microbial-metabolites-in-the-four-mw-agricultural-biogas-plant-reactors-a-case-study-of-inhibition-mirroring-the-acute-rumen-acidosis-symptoms
#12
Boštjan Murovec, Damjan Makuc, Sabina Kolbl Repinc, Zala Prevoršek, Domen Zavec, Robert Šket, Klemen Pečnik, Janez Plavec, Blaž Stres
In this study, nuclear magnetic resonance (1 H NMR) spectroscopic profiling was used to provide a more comprehensive view of microbial metabolites associated with poor reactor performance in a full-scale 4 MW mesophilic agricultural biogas plant under fully operational and also under inhibited conditions. Multivariate analyses were used to assess the significance of differences between reactors whereas artificial neural networks (ANN) were used to identify the key metabolites responsible for inhibition and their network of interaction...
September 15, 2018: Journal of Environmental Management
https://www.readbyqxmd.com/read/29875778/mu-loc-a-machine-learning-method-for-predicting-mitochondrially-localized-proteins-in-plants
#13
Ning Zhang, R S P Rao, Fernanda Salvato, Jesper F Havelund, Ian M Møller, Jay J Thelen, Dong Xu
Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations...
2018: Frontiers in Plant Science
https://www.readbyqxmd.com/read/29870348/multi-organ-plant-classification-based-on-convolutional-and-recurrent-neural-networks
#14
Sue Han Lee, Chee Seng Chan, Paolo Remagnino
Classification of plants based on a multi-organ approach is very challenging. Although additional data provide more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Despite promising solutions built using deep learning enable representative features to be learned for plant images, the existing approaches focus mainly on generic features for species classification, disregarding the features representing plant organs...
September 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29797888/-nondestructive-detection-of-total-nitrogen-content-in-leaves-of-santalum-album-based-on-st-pca-bp-neural-network
#15
Zhu Lin Chen, Xue Feng Wang
Nitrogen is one of the most important elements for plant growth. Producers often use a lot of nitrogen fertilizer during plant growth process. However, excessive fertilizer often cause ground-water pollution. In this study, we proposed a nondestructive testing method for total nitrogen content in leaves of sandalwood (Santalum album) based on ST-PCA-BP neural network. The results showed that, due to the wide color range of L* a* b* color system and its robustness in illumination change, images obtained from the field which were converted from RGB to L* a* b* color system had a satisfying segmentation result...
May 2018: Ying Yong Sheng Tai Xue Bao, the Journal of Applied Ecology
https://www.readbyqxmd.com/read/29783642/a-novel-locating-system-for-cereal-plant-stem-emerging-points-detection-using-a-convolutional-neural-network
#16
Hadi Karimi, Søren Skovsen, Mads Dyrmann, Rasmus Nyholm Jørgensen
Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the PSEPs of the cereal plants were marked manually and used to train a fully-convolutional neural network. In the training process, a cost function was made, which incorporates predefined penalty regions and PSEPs...
May 18, 2018: Sensors
https://www.readbyqxmd.com/read/29734699/implementation-of-cyber-physical-production-systems-for-quality-prediction-and-operation-control-in-metal-casting
#17
JuneHyuck Lee, Sang Do Noh, Hyun-Jung Kim, Yong-Shin Kang
The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed...
May 4, 2018: Sensors
https://www.readbyqxmd.com/read/29723047/statistical-optimization-of-the-phytoremediation-of-arsenic-by-ludwigia-octovalvis-in-a-pilot-reed-bed-using-response-surface-methodology-rsm-versus-an-artificial-neural-network-ann
#18
Harmin Sulistiyaning Titah, Mohd Izuan Effendi Bin Halmi, Siti Rozaimah Sheikh Abdullah, Hassimi Abu Hasan, Mushrifah Idris, Nurina Anuar
In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg-1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0...
June 7, 2018: International Journal of Phytoremediation
https://www.readbyqxmd.com/read/29672214/use-of-artificial-neuronal-networks-for-prediction-of-the-control-parameters-in-the-process-of-anaerobic-digestion-with-thermal-pretreatment
#19
Rita Flores-Asis, Juan M Méndez-Contreras, Ulises Juárez-Martínez, Alejandro Alvarado-Lassman, Daniel Villanueva-Vásquez, Alberto A Aguilar-Lasserre
This article focuses on the analysis of the behavior patterns of the variables involved in the anaerobic digestion process. The objective is to predict the impact factor and the behavior pattern of the variables, i.e., temperature, pH, volatile solids (VS), total solids, volumetric load, and hydraulic residence time, considering that these are the control variables for the conservation of the different groups of anaerobic microorganisms. To conduct the research, samples of physicochemical sludge were taken from a water treatment plant in a poultry processing factory, and, then, the substrate was characterized, and a thermal pretreatment was used to accelerate the hydrolysis process...
April 19, 2018: Journal of Environmental Science and Health. Part A, Toxic/hazardous Substances & Environmental Engineering
https://www.readbyqxmd.com/read/29660725/modeling-lead-concentration-in-drinking-water-of-residential-plumbing-pipes-and-hot-water-tanks
#20
Shakhawat Chowdhury, Fayzul Kabir, Mohammad Abu Jafar Mazumder, Md Hasan Zahir
Drinking water is a potential source of exposure to lead (Pb), which can pose risk to humans. The regulatory agencies often monitor Pb in water treatment plants (WTP) and/or water distribution systems (WDS). However, people are exposed to tap water inside the house while water may stay in the plumbing premise for several hours prior to reaching the tap. Depending on stagnation period and plumbing premise, concentrations of Pb in tap water can be significantly higher than the WDS leading to higher intake of Pb than the values from WDS or WTP...
September 1, 2018: Science of the Total Environment
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