KLeFHNet: Kronecker LeNet Forward Harmonic Net for Crop Recommendation With Routing in Wireless Sensor Networks for Agriculture

ABSTRACT A wireless sensor network (WSN) is deployed to monitor agricultural environments, helping to overcome issues like suboptimal crop selection that can result in lower yields and diminished quality. To enhance crop recommendation, this work proposes the Kronecker LeNet Forward Harmonic Net (KLeFHNet). The workflow begins with WSN simulation and energy prediction using a DRNN, after which clusters are formed using the Dung Namib Optimizer (DNO) and routing is handled by the Fractional DNO (FDNO). The combination of data normalization, Wave–Hedges metric‐based feature selection, and oversampling for data augmentation boosts the performance of the model. The integration of the Deep Kronecker Network, LeNet, and Forward Harmonic Network within KLeFHNet boosts energy efficiency and prolongs network longevity. Experimental results show that FDNO achieves an energy consumption of 0.353 J, a network lifetime of 74.271, and a trust of 86.266, whereas KLeFHNet attains a TPR of 0.900, TNR of 0.895, and FPR of 0.105, supporting sustainable, data‐driven agriculture.