Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Simultaneous analysis of economic and environmental performance of horticultural crop production requires qualified assumptions on the effect of management options, and particularly of nitrogen (N) fertilisation, on the net returns of the farm. Dynamic soil-plant-environment simulation models for agro-ecosystems are frequently applied to predict crop yield, generally as dry matter per area, and the environmental impact of production. Economic analysis requires conversion of yields to fresh marketable weight, which is not easy to calculate for vegetables, since different species have different properties and special market requirements. Furthermore, the marketable part of many vegetables is dependent on N availability during growth, which may lead to complete crop failure under sub-optimal N supply in tightly calculated N fertiliser regimes or low-input systems. In this paper we present two methods for converting simulated total dry matter to marketable fresh matter yield for various vegetables and European growth conditions, taking into consideration the effect of N supply: (i) a regression based function for vegetables sold as bulk or bunching ware and (ii) a population approach for piecewise sold row crops. For both methods, to be used in the context of a dynamic simulation model, parameter values were compiled from a literature survey. Implemented in such a model, both algorithms were tested against experimental field data, yielding an Index of Agreement of 0.80 for the regression strategy and 0.90 for the population strategy. Furthermore, the population strategy was capable of reflecting rather well the effect of crop spacing on yield and the effect of N supply on product grading

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Not AvailableGreen synthesis of nanoparticles by using different biological agents has emerged as an alternative to overcome the toxic effect of chemically synthesized nanoparticles. Among various biological agents, plants are mostly preferred. This study describes an eco-friendly and green synthesis of silver nanoparticles (G-AgNPs) using Azadirachta indica (neem) as a reducing agent. UV–Vis spectral analysis proved the wavelength of sample to be 420 nm, approaching the surface resonance peak specific for G-AgNPs. Dynamic light scattering (DLS) analysis showed the mean diameter of particles as 35.4 nm with zeta potential +34.6 mV. TEM results revealed the compact and spherical shape of the particles. Fourier transform infrared spectroscopies (FT-IR) demonstrate the presence of possible functional groups involved in synthesis of the silver nanoparticles. The functional activity of immunological parameters, such as nitroblue tetrazolium assay, myeloperoxidase activity, phagocytic activity, anti-protease and lysozyme activity, increased significantly (P < 0.05) in fish treated with G-AgNPs. Relative percentage survival (74%) and enhanced disease resistance were observed in G-AgNP-treated Cirrhinus mrigala fingerlings challenged with Aeromonas hydrophila. In summary, present results demonstrate biosynthesized silver nanoparticles have immunomodulatory and antibacterial activity.Not Availabl

Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indicators, sentiment analysis etc to predict the future stock price. In recent years, many researches have extensively used machine learning for predicting the stock behaviour. In this paper we propose data driven deep learning approach to predict the future stock value with the previous price with the feature extraction property of convolutional neural network and to use Neural Arithmetic Logic Units with it.Comment: Accepted at ICACDS 2019 - Springer CCI