A STUDY OF ENVIRONMENTAL FACTORS AFFECTING THE GROWTH AND PRODUCTION OF BARLEY PLANT BY USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY | ||||
| Arab Universities Journal of Agricultural Sciences | ||||
| Article 11, Volume 27, Issue 3, September 2019, Page 1843-1851 PDF (1.33 MB) | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/ajs.2019.14369.1057 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Shaimaa mohammed Baraka   1; Mohamed Nabil El-Awadi1; Zeinab Hussein Behairy2; Mohamed Abdel-Magid Genaidy 3
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| 1agricultural engineering, agriculture faculty, Ain shams university, Cairo, Egypt | ||||
| 2Horticulture Department, Agriculture Faculty, Ain Shams University, Cairo, Egypt | ||||
| 3Agricultural Engineering , Agriculture Faculty, Ain Shams University, Cairo, Egypt | ||||
| Abstract | ||||
| The impact of climate on crop production has vital importance. Climate variables affect the different crops during different stages of the growth and the development. This research aims to study the environmental factors affecting the growth and production of barley (Hordeum Sp., Gramineae) in a hydroponic system, to provide information to farmers and decision makers by using Artificial Neural Network (ANN) Model for production prediction. Multilayer feed-forward ANN (fully connected) was used in supervised manner and the training method was the back-propagation algorithm by using MATLAB program. The inputs in the ANN model of barley were: seeds density (kg/m2), lighting duration (h/day), light intensity (Lux), temperature (cº), relative humidity (%) and growing period (days). The outputs were: plant length (cm), yield (kg/m2), protein (%), dry matter (%), and conversion factor. Results revealed that the optimal configuration for the ANN model consisted of four layers (6-25-30-5). The hidden layers had 25 and 30 nodes in the first and second hidden layers respectively for the ANN model. Hyperbolic tangent transfer function was employed in hidden and output layers of the ANN model. The learning rate and the momentum parameter were 0.005 and 0.9 respectively for the ANN model. Iterations were 10000 epochs during training process for the ANN model. The results showed that the variation between target and predicted outputs was small while the correlation coefficient (R) was 0.99. Also, the results revealed that the major parameters affecting on all the outputs were seeds density and the duration of the lighting followed by the other factors i.e. temperature (cº), relative humidity (%), growing period (days) and light intensity (Lux). Seeds density has a higher percent relative importance, on yield, plant length, protein (%), DM (%) and conversion factor equal to 22.8%, 24%, 25%, 24% and 22.8% respectively. The developed ANN model was beneficial tool for barley production prediction. The barley yield prediction could be helpful for farmers, decision makers and planning to manage their crop better by providing a series of recommendations about crops planting and clarifying its impact on changes to these factors under the study in order to avoid losses and reach the best benefit (maximization of yield). | ||||
| Keywords | ||||
| artificial neural network; Barley; Environmental factors | ||||
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