Call For Paper Volume: V, Issue: 08 | AUGUST 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume V | Issue 6 | 2026 | Paper ID: IJATEM-V05I06P4

A Whale Optimization Algorithm Enabled CNN-GRU-Mamba Hybrid Framework for Precise Soil Fertility Classification Using Agricultural Soil Parameters

, P. Kavitha

Soil Fertility (SF) classification is important for sustainable agriculture, efficient nutrient management, and improved crop productivity. In this paper a Whale Optimization Algorithm-enabled Convolutional Neural Network - Gated Recurrent Unit -Mamba (WOA-CNN-GRU-Mamba) hybrid structure is proposed for precise SF classification using agricultural soil parameters. Data pre-processing based on missing-value handling and duplicate removal, data visualization based on outlier detection is used to improve data quality. Relevant soil attributes are then transformed through feature engineering and normalization to provide reliable inputs for model learning. The proposed hybrid model integrates CNN for extracting local feature patterns, GRU for learning sequential dependencies, and Mamba for capturing long-range relationships efficiently. Furthermore, the WOA is used to optimize the model parameters and improve classification performance and convergence. Experimental results is demonstrated in Python software using SDN Intrusion Detection dataset the proposed structure attains an 98% for accuracy, precision, F1-score, as well as recall of 97% ROC-AUC of 100%. The proposed WOA-CNN-GRU-Mamba framework provides an accurate and computationally efficient solution for intelligent SF assessment and support precision agriculture and sustainable soil management.

Soil Fertility, Agricultural Soil Parameter, Whale Optimization Algorithm, Convolutional Neural Network, Gated Recurrent Unit, Mamba

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