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Soil nutrients are essential for the growth of healthy crops. India producesa humungous quantity of Mulberry leaves which in turn produces the raw silk.Since the climatic conditions in India is favourable, Mulberry is grownthroughout the year. Majority of the farmers hardly pay attention to the natureof soil and abiotic factors due to which leaves become malnutritious and thuswhen they are consumed by the silkworm, desired quality end-product, raw silk,will not be produced. It is beneficial for the farmers to know the amount ofyield that their land can produce so that they can plan in advance. In thispaper, different Machine Learning techniques are used in predicting the yieldof the Mulberry crops based on the soil parameters. Three advancedmachine-learning models are selected and compared, namely, Multiple linearregression, Ridge regression and Random Forest Regression (RF). Theexperimental results show that Random Forest Regression outperforms otheralgorithms.

Mulberry Leaf Yield Prediction Using Machine Learning Techniques