An Intelligent Decision Support System for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms - Maryum Bibi; Rehman, Saif Ur; Khalid Mahmood; Rana Saud Shoukat (Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 2023)
Agriculture is crucial to economic growth and development. Crop yield forecasting is critical for food production which includes vegetables, fruits, flowers, and cattle. Artificial Intelligence (AI) is rising in agriculture, providing farmers with real-time or long-term insights about their fields. It allows us to identify the areas that require irrigation, fertilization, or pesticide treatment. Statistical models struggle to track complex relationships in crop yields due to numerous factors. Machine Learning (ML) and Deep Learning (DL) algorithms can solve this problem by training themselves in these relationships, enabling accurate predictions in agricultural yield prediction methods. Predicting product performance in agriculture is challenging due to various factors, but profit forecasting improves decision-making, production, economics, and food safety. The present study focuses on the use of ML and DL algorithms to suggest a novel decision support system for crop yield prediction with the objectives to develop a robust, accurate model, investigate algorithm effectiveness, and create a user-friendly system for informed crop production decisions. According to the results, the developed system is capable of making precise predictions, which can support farmers in making better decisions about how to manage their crops. The simulation results demonstrate that the intelligent decision support system proposed for crop yield prediction using ML and DL algorithms is capable of achieving high accuracy and precision. The system can be used to help farmers make better decisions about crop planting and management, which can lead to increased crop yields and profits. The results of our experiment show that our model is better than the others and it achieves an accuracy of 99.82 %. Additionally, we utilized ML to condense the input space while preserving high accuracy.