Abstract

Agriculture is called as the critical sector of the Indian economy. It feeds around 70 percent of the population and contributes significantly to national GDP (17 percent). Using advanced technologies to improve prediction, monitoring, and control in modern agricultural practices is challenging. The research looks at how Deep Learning (DL), Machine Learning (ML), and Internet of Things (IoT), work together to accurately predict crop yield. To optimize predictive models, various algo-rithms such as Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Decision Trees, Support Vector Machines (SVM), Random For- est, k-Nearest Neighbors (KNN), and Naive Bayes are used. When using real-time sensor data from various sources, ANN and Random Forest outperform. Remote sensing technologies, weather forecasting systems, satellite imagery, and other relevant repositories are used to generate training datasets. However, problems such as insufficient training datasets pose significant challenges, leading to concerns such as overfitting and poor model performance. Addressing these limitations is critical for improving the dependability and effectiveness of predictive mod- els in dynamic agricultural settings. The combination of IoT, ML, and DL represents a revolutionary development in agricultural practices, particularly crop yield prediction. Continuous research is required to overcome obstacles and improve algorithmic approaches. The prevalence of ANN and Random Forest high- lights their potential to elevate decision support systems in agriculture, emphasizing the importance of ongoing research and improvement.