Enhancing Crop Yield Prediction Using Ensemble Learning Techniques on Multi-Modal Agricultural Data - Kishore, Pinninti; Kumar, Rakesh; Negi, Harendra Singh; Sharma, Kamal; Srivastava, Arun Pratap; Kumar, Navneet; Shrivastava, Anurag (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This exploration examines the viability of outfit learning methods on multi-modular horticultural data to further develop crop yield expectation exactness. Using different datasets including soil properties, weather conditions, crop wellbeing markers, and agronomic practices, we assessed four group methods: Random Forest, Gradient Boosting, Outrageous Gradient Boosting, and AdaBoost. Results exhibit that Outrageous Gradient Boosting outflanks different methods, accomplishing a Mean Squared Error (MSE) of 900, R-squared (R2) of 0.85, and Mean Absolute Error (MAE) of 22. Near examination against gauge models outlines the predominant prescient exactness and power of gathering methods. The computational proficiency of group strategies stays similar to or better than conventional models, with preparing times going from 120 to 200 seconds and memory utilization somewhere in the range of 500 and 700 MB. This study highlights the capability of outfit learning in farming navigation, offering experiences into ideal prescient displaying procedures for improving harvest yield expectation and supporting manageable agrarian practices.