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Enhancing efficiency in agriculture: densely connected convolutional neural network for smart farming

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Abstract

Smart farming plays a revolutionary paradigm shift in the realm of agriculture to enhance farming practices across the globe. Traditional smart farming methods face various challenges such as inefficiency, resource wastage, weather dependency, and environmental impact. To overcome these challenges, this paper proposed a novel method named Densely Connected Convolutional based Golden search (DCC-GS) with initial Search strategy for smart farming in agriculture. In this study, a densely connected convolutional network is utilized to capture intricate patterns in the data which is significant for smart farming. In this work, the Golden search optimization algorithm is utilized for hyperparameters tuning and enhancing the effectiveness of the DCC-GS method. Also, an initial search strategy is implemented to decrease the computational burden of the DCC-GS method. The DCC-GS method is validated on the Crop Recommendation dataset. The performance evaluation of the DCC-GS method involves analyzing its effectiveness based on key measures such as cost, specificity, recall, F1-score, precision, and accuracy and comparing its performance against existing methods to assess the DCC-GS method’s effectiveness. The DCC-GS method achieved accuracy, precision, recall, F1-score, and specificity of 98.63%, 97.95%, 97.83%, 97.86%, and 97.82% respectively. The experimental evaluation illustrates the effectiveness of the DCC-GS method for smart farming in agriculture. Moreover, Recognizing the importance of agriculture in global food security, many farmers are motivated to adopt smart farming practices to contribute to stable and sufficient food supplies.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Contributions

VP agreed on the content of the study. AS,VP,DK,RR collected all the data for analysis. VP agreed on the methodology. AS, VP, DK, RR completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to P. Valarmathie.

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Sivaraj, A., Valarmathie, P., Dinakaran, K. et al. Enhancing efficiency in agriculture: densely connected convolutional neural network for smart farming. SIViP 18, 6469–6480 (2024). https://doi.org/10.1007/s11760-024-03330-x

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