Artificial Neural Network to Determine the Optimum Nutrient Media Composition in Plant Biotechnology

Tapish Dongre¹* and Bharatlal Choudhary²

¹Biotechnology, Queensland University of Technology, Australia
²Biotechnology, Nagpur University, India

*Corresponding Author: Tapish Dongre, Biotechnology, Queensland University of Technology, Australia.

Received: July 02, 2018; Published: July 25, 2018

Citation: Tapish Dongre and Bharatlal Choudhary. “Artificial Neural Network to Determine the Optimum Nutrient Media Composition inPlant Biotechnology". Acta Scientific Agriculture 2.8 (2018).

Abstract

  Growth of various plants in Agriculture industry, whether on-field crop or a plant tissue culture laboratory specimen requires resources and time to obtain the first yield. In traditional way of plant tissue culture huge amount of the resources as well as time is consumed only to adjust growth condition for the minimal threshold of plant required, before actually taking a step further in experimental specimens. Every alteration in the nutrient media consumes further time and resources to obtain the required results by traditional trial and error method. These resources and time spent can be minimised if a virtual computational model can be made instead of performing the actual physical plant growth experiments. This led to the idea of using and creating an artificial neural network to execute the simulation of plant growth and respective yield performance on a computational machine and using those result to actually grow the plant in its optimum yield condition.

  Artificial Neural Network is computational technique which mimics the brain synapse signal model into a computer programme which self-learns and provides an output as according to the targeted results. When all inputs and its respective target result models are fed into this computational model and run with maximum accuracy, it then allows a user to try and put any random variable value of initial input and the corresponding result along with effect on actual specimen are displayed on screen. For example, if a computer has the information of what different concentration of ‘x’ nutrient is affecting the growth of plant, then by putting a value of any concentration of the ‘x’ nutrient as input will give the exact effect to plant as output. This simple example can be mathematically solved on a two-dimensional graph. But imagine when the medium is made out of several components like x, y, z, a, b, c and each effect the growth of plant. Thus, in this case change in concentration of any component can be modelled and calculated using artificial neural network saving time and other expensive resources to perform each and every experiment individually.

Keywords: Artificial Neural Network (ANN); Optimum; Plant; Nutrient Media; Plant Tissue Culture; Agricultural Advancement; Plant Biotechnology

Copyright: © 2018 Tapish Dongre and Bharatlal Choudhary. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.