Research Article | Open Access | Download PDF
Volume 73 | Issue 11 | Year 2025 | Article Id. IJETT-V73I11P117 | DOI : https://doi.org/10.14445/22315381/IJETT-V73I11P117Parameter Tuned Hybrid Deep Learning Network with Improved Algorithm-Assisted Weighted Feature Selection for Yield Prediction Using IoT Sensor in Agricultural Field
Sudharsan Nagendram, Sudarsanam S K
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 11 Apr 2025 | 01 Nov 2025 | 10 Nov 2025 | 25 Nov 2025 |
Citation :
Sudharsan Nagendram, Sudarsanam S K, "Parameter Tuned Hybrid Deep Learning Network with Improved Algorithm-Assisted Weighted Feature Selection for Yield Prediction Using IoT Sensor in Agricultural Field," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 11, pp. 227-251, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I11P117
Abstract
Crop yield prediction is inherently complex, determined by numerous issues including environment, genotype, and their interaction. Effective forecasting requires recognizing the functional criteria among interacting factors and yield, necessitating both robust algorithms and comprehensive datasets. Machine learning has become a crucial decision-making tool in agriculture, aiding in crop selection and cultivation management. Various machine learning techniques are employed to predict the crop yield. Among all these techniques, deep learning models offer improved accuracy in complex classes. In this work, applications of artificial intelligence techniques are explored with the Internet of Things (IoT) to enhance the prediction efficiency of crop yield. An automated and intelligent methodology, an adaptive classifier network, is employed. Data is collected from a benchmark database, and a Novel Parameter Wave Search Algorithm (NPWSA) optimizes and selects weighted features, which are then input into a Parameter-tuned Hybrid Network (PHNet). The PHNet model is built using a combination of a Pyramidal Dilated Convolutional Neural Network (PDCNN) and a Stacked Recurrent Neural Network (SRNN). The overall performance of the proposed technique is evaluated through several metrics. Experimental results demonstrate that NPWSA significantly improves prediction accuracy compared to conventional methods, contributing to enhanced crop productivity and improved economic outcomes for farmers.
Keywords
Internet of things, Novel parameter derived wave search algorithm, Pyramidal dilated convolutional neural network, Stacked recurrent neural network, Weighted features selection, Yield prediction.
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