Artificial Neural Networks and its Role in Plant Breeding Under Drought Stress

Volume 1 - Issue 2

Mohammad Reza Naroui Rad*

Received: January 30, 2018;   Published: February 09, 2018

DOI: 10.32474/CIACR.2018.01.000106

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Abstract

All plants need water for the process of photosynthesis to take place effectively. Lack of enough water in plants decreases stomata conductivity that limits the uptake of carbon dioxide which is a necessary element for photosynthesis. The best way of increasing yield and subsequent profit is by performing early detections and managing the problems relating to crop yield. It is, therefore, important to develop models to evaluate crop production behavior specifically weather as it helps in minimizing costs incurred through operation and analysis and evaluate stability In stimulating the egg plant relative water content’s response to condition of weather of sis tan, two models; Multilayer Perception (MLP) and Artificial Neural Network (ANN) together with some input variables such as; the height of a plant, weight of a fruit, length of a fruit, widths of a fruit, number of fruits, fruit lengths ratio to its width, chlorophyll, total yield and canopy temperature were developed. According to the results obtained from the ANN model, it was found that the ANN Model was the best. This is because the results obtained from; training, testing phase’s precision and validating had a very low absolute error of 0.035, 0.033 and 0.027 respectively for MLP 9-15- 1in egg plant. The highest correlation coefficient characteristics were the selected network at the concurrently low amplitude among the sets: validation, training, and the test one 0.83, 0.86 and 0.83 respectively.

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