Artificial neural networks to estimate, artichoke’s antioxidant components evaluation based on the easily available soil properties
Paper Details
Artificial neural networks to estimate, artichoke’s antioxidant components evaluation based on the easily available soil properties
Abstract
One of the most important requirements in planning production and processing of medicinal plants in order to obtain high yield and high-quality is the initial assessment of the soil physical and chemical properties, which can reduce the production cost by avoiding the use of unnecessary soil analysis. Artichoke (Cynara scolymus L.) is one of the useful and medical herbs which is considered as the plant qualitative index based on the secondary components like antioxidant components. Therefore, it is necessary to evaluate the yield performance of artichoke by means of fast and cheap methods with an acceptable accuracy. The present study aims at investigating the amount of antioxidants of artichoke by means of soil physical and chemical characteristics including: soil texture, percent of organic carbon, percent of neutralizing substances, pH, EC, CEC, phosphorus, potassium, nitrogen and apparent specific gravity by artificial neural network. So soil sampling conducted from 60 different agricultural and forest lands of Golestan Province, soil parameters measured in lab. Based on sensitive parameters different models have been designed. The results showed that all artificial neural network models were more efficient rather than multivariate regression model. The model 5 is selected with an overall view as an optimal model, as with a minimum input parameter with a function close to other models with the number of parameters. However, the number 4 model, because in the explanatory coefficient compared to the three models, will be chosen, especially in the case of the performance and cost of being selected, because with a test (soil texture), three parameters are measured. The results indicated that the neural network application was used to estimate antioxidant amount performance using soil parameters, but it is also suggested to continue to access the definitive results of similar research in this regard.
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Azadeh Alizadeh, 2020. Artificial neural networks to estimate, artichoke’s antioxidant components evaluation based on the easily available soil properties. Int. J. Biosci., 16(6), 98-120.
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