Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area

Leaf Area (LA) is a key index of plant productivity and growth. A multiple linear regression technique is commonly applied to estimate LA as a non-destructive and quick method, but this technique is limited under the realistic situation. Thus, it is indispensable to elaborate new models for estimation. In this research, the performance of the Adaptive Neural-Based Fuzzy Inference System (ANFIS) in predicting the LA of 61 plant species (C) was investigated. Four parameters including leaf length (L), leaf width (W), C, and specific coefficient (K) for each plant were selected as input data to the ANFIS model and the LA as the output. Seven different ANFIS models including different combinations of input data were constructed to reveal the sensitivity analysis of the models. The normalized root mean square error (NRMSE), mean residual error (MRE), and linear regression were applied between observed LA and estimated LA by the models. The results indicated that ANFIS4-K-2min which employed all input data was the most accurate (NRMSE=0.046 and R-2=0.997) and ANFIS1 which employed only the K input was the worst (NRMSE=0.452 and R-2=0.778). In ranking, ANFIS4-K-2ave, ANFIS4-K-1min, ANFIS4-K-1ave, ANFIS3, and ANFIS2 ranked second, third, fourth, fifth, and sixth, respectively. The sensitivity analysis indicated that the predicted LA is more sensitive to the K, followed by L, W, and C. The results displayed that estimations are slightly overestimated. This study demonstrated that the ANFIS model could be accurate and faster alternative to the available laborious and time-consuming methods for LA prediction.