Abstract
Technology of precision agriculture has caused to the remote sensors development that compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artificial Neural Networks (ANNs), as a soft computing methods, are one of the most efficient methods for computing as compared to the statistical and analytical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant analysis development (SPAD), protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in 2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS models.
Highlights
The crops monitoring can be performed for ground survey at a local scale
Rasooli Sharabian et al (27) used the multivariate analysis including of PLSR and SMLR to select the best wavelengths for growth characteristics of winter wheat
The main aim of this study was deployment of experimental Radial Basis Function (RBF) of ANNs models in comparison to adaptive neural-fuzzy inference (ANFIS) system to design a network in order to predict the SPAD, protein content and grain yield of wheat plant based on spectral reflectance value
Summary
The crops monitoring can be performed for ground survey at a local scale. But remote sensing can be appropriated for both in terms of spatial and temporal coverage at the regional scale (19, 24, 29). The results showed strong relationships between predicted and actual crop variables They suggested the SMLR as the best model, because it had the highest R2 value (0.85, 0.89 and 0.84 for SPAD, grain protein and yield, respectively) (16,27). The main aim of this study was deployment of experimental Radial Basis Function (RBF) of ANNs models in comparison to adaptive neural-fuzzy inference (ANFIS) system to design a network in order to predict the SPAD, protein content and grain yield of wheat plant based on spectral reflectance value. RBF and ANFIS models were checked out where A1,A2 and B1, B2 are the fuzzy sets for using the comparing parameters such as inputs x and y ,respectively, p1, q1, r1 and p2, correlation coefficient (r) and the root mean q2, r2 are the parameters of the output function that are specified during the training of ANFIS (12,23,28). Pearson correlation (r) is used to measure the linear correlation between two parameters (here are predicted and target variables)
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