eng Enviro Research Publishers Current Agriculture Research Journal 2347-4688 2321-9971 2026-10-08 14 3 27597 article Kaushik Hazarika 1 Abhijit Borah 1 Rejaul Hoque Bepary 1 Tabaruk Hussain 1 Saurajyoti Baishya 1 Department of Agricultural Engineering, Assam Agricultural University, Jorhat, Assam, india The mushroom drying experiments were carried out in a tray dryer at temperatures ranging from 50 to 70 °C with drying times from 6 to 10 hours, and sample thicknesses from 5 to 20 mm. The data obtained from the drying experiments were used to fit drying models and to train ANN and ANFIS for moisture content of the sample. A response surface methodology analysis was carried out to integrate experimental data with regression-based models. The drying rate increased significantly (p<0.05) with increasing temperature and decreased with decreasing sample thickness, which indicated a typical falling-rate drying behaviour. RSM provided significant regression fits (R2=0.9067), whereas ANN (3-10-1 architecture) and ANFIS models showed superior predicted accuracy with overall R² values of 0.9966 and 0.9953, respectively. Among the three models, ANN exhibited the best performance with the lowest mean square error, followed closely by ANFIS, while RSM was less accurate in capturing the nonlinear drying behaviour. These findings enable accurate prediction of nonlinear mushroom drying kinetics, providing for future process monitoring and optimisation strategies. https://www.agriculturejournal.org/volume14number3/response-surface-methodology-artificial-neural-network-and-adaptive-neuro-fuzzy-inference-system-for-oyster-mushroom-drying-kinetics/ ANN; ANFIS; Drying; Oyster Mushroom; RSM