PREDICTION OF SOIL PENETRATION RESISTANCE WITH THREE DIFFERENT ARTIFICIAL NEURAL NETWORKING METHODS

Creative Commons License

Ünal İ., Kabaş Ö., Sözer S., Çetin S.

ROMANIAN AGRICULTURAL RESEARCH, cilt.36, ss.447-456, 2022 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36
  • Basım Tarihi: 2022
  • Dergi Adı: ROMANIAN AGRICULTURAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.447-456
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Soil compaction is one of the major problems of the agricultural sector affecting negatively the soil structure and impedes plant root growth. Soil penetration resistance should be measured from many points of the production land to determine the effects of plant growth problems. Collection of soil penetration values from huge lands is time-consuming, tiring, and tedious for researchers. Also, the number of measured points to what extent will be sufficient to evaluation on whole production area is not clear. To eliminate this ambiguity, soil penetration values of the unmeasured points should be estimated to evaluate the whole land. Artificial Neural Networks (ANN) are one of the most popular mathematical computing and modeling method used to estimate unknown data with the help of known data. In this study, we collected 1603 samples of geographical position and soil penetration value from 40 cm depth within the 20 ha field. From the 1603 values, 24% records were selected for testing and the remaining 76% records were used for training. Soil penetration values of the unmeasured points were estimated using Generalized Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP) and Radial Bias Function (RBF) methods in MATLAB. In addition to mean squared error (MSE), root mean square error (RMSE) and mean absolute error (MAE) has been also used for evaluation of prediction accuracy on these methods. RBF results showed very good agreement between the predicted and the measured real values of soil resistance (MSE: 0.1608; RMSE: 0.3717; MAE: 0.3682).