Optimizing Crop Production Through Smart Agricultural Practices: A Comprehensive Study on Soil Attribute Analysis

This research paper explores the integration of state-of-the-art machine learning techniques for crop classification and yield prediction in agriculture. Leveraging an extensive dataset with diverse soil and climate parameters, the study navigates the complex agricultural terrain. Principal component analysis (PCA) is employed for dimensionality reduction, enhancing model interpretability and efficiency. Rigorous evaluation of various classifiers, including neighbors, support vector machine (SVM), logistic regression, random forest, gradient boosting, and LGBM models, reveal nuanced insights into their performance. The LGBM classifier emerges as the top performer, achieving over 99% accuracy. Despite this success, challenges persist, especially in distinguishing specific crop pairs, such as black gram and pigeon peas, jute and rice, and moth beans and lentils. The study emphasizes the LGBM model's effectiveness in crop classification while acknowledging areas requiring further refinement. These findings provide valuable insights for agricultural stakeholders, policymakers, and technologists, contributing to the discourse on sustainable farming practices and food security.