Significance of Feature Importance on Crop Prediction Using Machine Learning Models

This study introduces an approach to advancing sustainable agriculture through the application of Machine Learning and Deep Learning techniques, leveraging the Crop Recommender Dataset with Soil Nutrition. Aimed at optimizing crop yields while minimizing environmental impacts, the research employs a comprehensive analysis of soil nutrients, underscoring the pivotal role these elements play in agricultural productivity. By integrating advanced ML and DL models, including Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Neural Networks, parameters are fine-tuned to achieve unprecedented prediction accuracy. Through a detailed feature importance analysis, key soil nutrients critical in influencing crop yield predictions are identified. Results showcase exceptional performance with 1D-CNN obtaining a high AUC score of $99.45 \%$ followed by XGBoost with the original dataset and after feature reduction, SVM and 1D-CNN models achieved a high AUC score of $99.58 \%$ and $99.51 \%$, respectively. This paper contributes significant insights towards implementing efficient and sustainable farming practices, showcasing the potential of AI in revolutionizing precision agriculture. Findings not only pave the way for enhancing agricultural productivity but also for reducing environmental degradation, marking a step forward in the journey towards sustainable farming methodologies.