Evaluation of Machine Learning Models for Optimized Crop Recommendation

In this research paper, we evaluate the performance of several machine learning algorithms, including Random Forest, Decision Tree, Logistic Regression, and KNN, for soil data classification to enhance crop recommendations. The findings reveal that the Random Forest algorithm excels in accuracy and specificity compared to alternative methods. Notably, optimized crop recommendations derived from the Random Forest algorithm exhibit a significant enhancement in both crop yield and quality when contrasted with traditional approaches. This study endeavors to identify the most efficacious machine learning algorithms for predicting soil types based on their properties and recommending optimal crops for cultivation. Utilizing a dataset encompassing soil properties such as type, pH level, organic matter content, and nutrient composition from diverse farms and agricultural fields, we conducted thorough preprocessing and cleaning procedures to eliminate outliers and ensure data quality. The study underscores that the Random Forest algorithm surpasses other algorithms in soil data classification, achieving notable accuracy, precision, and recall scores. This performance demonstrates its capability to accurately predict crops based on their properties. Furthermore, hypothesis analysis revealed strong evidence of a significant relationship between temperature and rainfall with crop labels, suggesting the necessity of including these factors in predictive models or further analyses. However, selecting the ideal model without considering the real-world environment risks becoming a matter of ill-supported opinion, as noted in prior research (Cortes and Vapnik, 1995). This study provides farmers and agricultural practitioners valuable insights on utilizing machine learning algorithms in soil data classification and crop recommendation to enhance agricultural production optimization.