Autonomous Smart Irrigation for Paddy Farming Using AI-Enhanced Dense Neural Networks and IoT-Based Soil Moisture Control
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This study focuses on the use of artificial intelligence (AI), in the form of dense neural networks (DNNs) to the improvement of irrigation practices in paddy agriculture. To obtain a high fidelity data set with 420 time ordered data records which include temperature, relative humidity, wind velocity, atmospheric pressure, meteorological parameters and soil moisture tension, a customized Internet of Things (IoT) based data acquisition system was designed and deployed in the field. A specialized DNN, considering the binary cross-entropy loss function, was used to predict the irrigation requirements with five hidden dense layers mixed with dropouts to overcome over fitting to represent a methodology for supplementing crop monitoring and management in the smart farming concept. Performance evaluation, which was done on the basis of accuracy, precision, recall, and F1 score, provided 99.17 percent accuracy, 99.25 percent precision, 99.12 percent recall, and 99.19 percent F1 score, which proved the model's ability to predict the irrigation necessity with great efficiency. The findings highlight the potential of DNNs in enabling data-driven, temporally accurate decision-making in agriculture, with the benefit of improved yield and water consumption. System autonomy is ensured by the activation of a water pump when soil moisture tension drops below 10 kPa and the termination of irrigation when moisture reaches or exceeds the threshold value, having precise water delivery. Moreover, the study highlights the synergy of IoT and AI as a route towards sustainable farming, as well as water conservation, optimization of resources, development of efficient and technology-based agricultural methods.
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