IoT-Enabled Soil-Integrated Crop Yield Predictor (IoT-SICYP): Advancing Precision Agriculture through Integration of IoT and Deep Learning

Abstract

By investigating the network between the Internet of Things (IoT) and deep learning methods to revolutionize precision agriculture, this paper derives a solid framework of a neural agri mapper with the IoT for forecasting crop yield algorithms. This research aims to advance precision agriculture by integrating IoT and deep learning methods to develop a neural agromapper for forecasting crop yields, focusing on optimizing model accuracy, scalability, and practical applicability by utilizing real-time sensor data and advanced analytics techniques. The methodology creates and trains the deep neural network model for crop guidance. The model in this research was specifically trained on the IoT-SICYP dataset. Based on its satisfactory accuracy of 88.7%, the crop recommendation system has the highest accuracy among all currently available techniques, such as logistic regression, K nearest neighbor, support vector machine, random forest, and decision tree methods. Hence, the proposed system is a deep neural network for prediction. The results show that the model provides recommendations according to the present characteristics of our dataset with the aid of the IoT-SICYP agri bot. However, it can be further optimized to provide more accurate outputs. This paper contributes to research by demonstrating the practicality of deep learning in crop yield prediction.