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"neural network" AND plant

Alina Bora, Takahiro Suzuki, Simona Funar-Timofei
Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids...
March 14, 2019: Environmental Science and Pollution Research International
Ivan Pisa, Ignacio Santín, Jose Lopez Vicario, Antoni Morell, Ramon Vilanova
Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water's pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t )...
March 13, 2019: Sensors
Wang Dawei, Deng Limiao, Ni Jiangong, Gao Jiyue, Zhu Hongfei, Han Zhongzhi
BACKGROUND: Plant pest of insects mainly refers to insects and mites that harm crops and products. It has a wide variety, wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. So pest recognition is very importance to crops growing healthily, and this in turn affects crop yields and quality. At present, it is a great challenge to realize accurate and reliable pest identification. RESULTS: In this study, we put forward a diagnostic system based on transfer learning for pest detection and recognition...
March 14, 2019: Journal of the Science of Food and Agriculture
Maria Polinova, Lea Wittenberg, Haim Kutiel, Anna Brook
Wildfires occurring near and within cities are a potential threat to the population's life and health and can cause significant economic damage by destroying infrastructure and private property. Due to the relatively small area of these wildlands, the accuracy of fire risk-assessment plays a significant role in fire management. Introducing the experience of real events can improve accuracy. But this approach is limited by a lack of knowledge of pre-fire conditions, mainly vegetation characteristics as related to their definition as a fuel parameter because of their high temporal variation...
March 6, 2019: Journal of Environmental Management
Linghuan Kong, Wei He, Chenguang Yang, Zhijun Li, Changyin Sun
In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning...
March 6, 2019: IEEE Transactions on Cybernetics
Sheng Wen, Quanyong Zhang, Xuanchun Yin, Yubin Lan, Jiantao Zhang, Yufeng Ge
Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained...
March 5, 2019: Sensors
Deyani Nocedo-Mena, Carlos Cornelio, María Del Rayo Camacho-Corona, Elvira Garza-Gonzalez, Noemi Herminia Waksman, Sonia Arrasate, Nuria Sotomayor, Esther Lete, Humbert González-Díaz
Predicting the activity of new chemical compounds over pathogenic microorganisms with different Metabolic Reaction Networks (MRNs) is an important goal due to the different susceptibility to antibiotics. ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, Leong & Barabàsi data set includes >40 MRNs of microorganisms...
February 25, 2019: Journal of Chemical Information and Modeling
Zeinab Ghaedrahmat, Mehdi Vosoughi, Yaser Tahmasebi Birgani, Abdolkazem Neisi, Gholamreza Goudarzi, Afshin Takdastan
In recent years, concerns over the issue of air pollution have increased as one of the significant environmental and health problems. Air pollutants can be toxic or harmful to the life of plants, animals, and humans. Contrast to primary pollutants, ozone is a secondary pollutant that is produced by the reaction between primary precursors in the atmosphere. The average of air pollutant data was compiled for the purpose of analyzing their correlation with the pulmonary function of students and the FENO biomarker from the air pollutants of the Environmental Protection Agency...
February 20, 2019: Environmental Science and Pollution Research International
Maryam Zeinolabedini, Mohammad Najafzadeh
In this study, artificial neural networks (ANNs) including feed forward back propagation neural network (FFBP-NN) and the radial basis function neural network (RBF-NN) were applied to predict daily sewage sludge quantity in wastewater treatment plant (WWTP). Daily datasets of sewage sludge have been used to develop the artificial intelligence models. Six mother wavelet (W) functions were employed as a preprocessor in order to increase accuracy level of ANNs. In this way, a 4-day lags were considered as input variables to conduct training and testing stages for the proposed W-ANNs...
February 16, 2019: Environmental Monitoring and Assessment
Ali Kaab, Mohammad Sharifi, Hossein Mobli, Ashkan Nabavi-Pelesaraei, Kwok-Wing Chau
This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production...
February 6, 2019: Science of the Total Environment
David Linnard Wheeler, Jeness Scott, Jeremiah Kam Sung Dung, Dennis Allen Johnson
Disease prediction tools improve management efforts for many plant diseases. Prediction and downstream prevention demand information about disease etiology, which can be complicated for some diseases, like those caused by soilborne microorganisms. Fortunately, the availability of machine learning methods has enabled researchers to elucidate complex relationships between hosts and pathogens without invoking difficult-to-satisfy assumptions. The etiology of a destructive plant disease, Verticillium wilt of mint, caused by the fungus Verticillium dahliae was reevaluated with several supervised machine learning methods...
2019: PloS One
Liang Han, Guijun Yang, Huayang Dai, Bo Xu, Hao Yang, Haikuan Feng, Zhenhai Li, Xiaodong Yang
Background: Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas...
2019: Plant Methods
Yufeng Fan, Xiaodong Zhu, Hulin Sui, Haotai Sun, Zhongming Wang
In recent years, fire accidents in petrochemical plant areas and dangerous goods storage ports in China have shown a trend of frequent occurrence. Toxic and harmful gases are diffused in the scenes of these accidents, which causes great difficulties for fire fighting and rescue operations of fire fighting forces, and consequently, casualties of firefighters often occur. In order to ensure the safety of firefighters in such places, this paper designs a monitoring system of toxic and harmful gases specially used in fire fighting and rescue sites of fire forces, and establishes the transmission network, monitoring terminal and data processing software of the monitoring system of toxic and harmful gases, establishing the danger model of the monitoring area of toxic and harmful gas-monitoring terminal, and the danger model of fire fighters' working area, fusing the field toxic and harmful gas data, terminal positioning data, and field environmental data, designing the data structure of the input data set and the network structure of the RNN cyclic neural network model, and realizing the dynamic early warning of toxic and harmful gases on site...
January 17, 2019: Sensors
Vahid Nourani, Gozen Elkiran, S I Abba
In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff ), chemical oxygen demand (CODeff ) and total nitrogen (TNeff ). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods...
December 2018: Water Science and Technology: a Journal of the International Association on Water Pollution Research
Lorenzo Rossi, Majid Bagheri, Weilan Zhang, Zehua Chen, Joel G Burken, Xingmao Ma
Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeO2 NPs) are now used in a plethora of industrial products, while cadmium (Cd) is a great environmental concern because of its toxicity to animals and humans. Up to now, the interactions between heavy metals, nanoparticles and plants have not been extensively studied. The main objectives of this study were (i) to determine the synergistic effects of Cd and CeO2 NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using Artificial Neural Network (ANN) as an alternative methodology to modeling and simulating plant uptake of Ce and Cd...
December 12, 2018: Environmental Pollution
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
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