Artificial Neural Networks and its Role in Plant Breeding Under Drought Stress by Mohammad Reza Naroui Rad in Current Investigations in Agriculture and Current Research in Lupine Publishers
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
9-151in 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.
https://www.lupinepublishers.com/agriculture-journal/abstracts/artificial-neural-networks-and-its-role-in-plant-breeding-under-drought-stress.ID.000106.php
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