Optimi1zing Soil Moisture Prediction and Crop Yield Enhancement for Medicinal Plants Using Convolutional Neural Networks in Precision Agriculture

Farms undergo transformation through artificial intelligence integration in precision agriculture which results in improved crop yields together with enhanced resource management. The ability to forecast soil moisture alongside medicinal plant yield remains difficult because both climate change variability and conventional techniques display their limitations. The proposed CNN model represents a new method for effective multivariate analysis of multispectral as well as hyperspectral imaging information with real-time IoT sensor data collection to achieve superior prediction results. Our CNN-based system achieves environmental adaptation and soil-plant interaction learning through 100 training epochs which leads to a considerable mean absolute error reduction down to 2,513.33 from the initial value of 2,621.88. The proposed CNN framework achieves better accuracy results than current model frameworks at 0.95 versus their previous 0.90. Research will analyses combined artificial neural networks using CNN-LSTM and ensemble methods to secure better optimization outcomes. The research development achieves precision agriculture by establishing real-time high-accuracy tools for medicinal crop soil moisture prediction and yield assessment which supports sustainable farming practices.