Deep Learning-Driven Precision Agriculture: Enhancing Crop Recommendation and Soil Analysis through Advanced IoT Sensor Data
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This research investigates the optimization of crop recommendation and soil type analysis through a fusion of cutting-edge deep learning algorithms, encompassing Convolutional Neural Networks (CNNs), Capsule Networks (CapsNets), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, and an innovative AdaBoostClassifier integrated with GRU. Leveraging an extensive dataset collected from IoT sensors measuring critical agricultural parameters such as soil moisture, temperature, pH levels, and nutrient content, this study explores the intricate relationships between these factors. The CNN extracts spatial features, CapsNets unravel complex soil patterns, while GRUs and LSTMs capture temporal dynamics and sequential dependencies within the data. The proposed AdaBoostClassifier coupled with GRU attains a remarkable 99% accuracy in crop recommendation, showcasing its effectiveness. These deep learning architectures, integrated with IoT sensor data, offer a robust framework for precision agriculture, empowering farmers with accurate crop suggestions based on soil conditions, thereby fostering enhanced agricultural productivity and sustainability.
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