Random Forest with lag variables recommended crops with 83.62% accuracy on out-of-sample 2014 data, the best honest temporal evaluation in the paper.
It exhibits a remarkable accuracy of 99.96% in Approach 1 followed by 78.55 % in Approach 2 and 83.62% in Approach 3
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Crop recommendation with machine learning: leveraging environmental and economic factors for optimal crop selection
Random Forest with lag variables recommended crops with 83.62% accuracy on out-of-sample 2014 data, the best honest temporal evaluation in the paper.