Comparative Analysis of Neural Networks and Deep Learning Techniques for Enhancing Precision in Agricultural Forecasting

As we know, India is primarily known for its farming practices. More than 50% of the total land is used for agriculture. The selection of land depends on the type of soil, climate conditions, and irrigation facilities. Agricultural methods have been used since 9000 BCE in northwest India, but traditional methods no longer adequately fulfill the needs due to population increase and adverse atmospheric conditions. There is a need to create a sustainable production system to meet our needs. The most advanced machine learning technology, known as deep learning (DL), excels in decision-making, computer vision, bioinformatics, natural language processing, and other fields. In this paper, we study how various deep learning algorithms can assist in predicting yields or any decision-making process. This paper implemented and compared various machine learning and deep learning algorithms to check the better precision of algorithms such as Decision Tree gives accuracy of 73.152%, Navies Bayes 49.020%, Random Forest 66.673%, k-nearest neighbors algorithm has 37. 581% and Support vector machine has 78.924% accuracy. Along with the comparison of algorithms with accuracies, Mean Absolute Error (MAE), Relative Absolute Error (RAE) and Root Mean Square Error (RMSE) are also compared.