SOIL FERTILITY PREDICTION AND CROP RECOMMENDATION USING ML ALGORITHM
Abstract
India's economy is heavily dependent on rising agricultural yields and agroindustry goods. In this paper, we explore various machine learning techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made. The outcome of the learning process is used by farmers for corrective measures for yield optimization. The Random Forest [RF] model provide suggestions for enhancing soil fertility and to recommend fertilizer depending on the soil's nutrient composition. Keywords: Machine Learning, Soil fertility prediction, Crop Recommendation.
How to Cite
Guruprasad Kulkarni, Ashwini, Diksha, Kavyanjali. (1). SOIL FERTILITY PREDICTION AND CROP RECOMMENDATION USING ML ALGORITHM. International Journal Of Innovation In Engineering Research & Management UGC APPROVED NO. 48708, EFI 5.89, WORLD SCINTIFIC IF 6.33, 11(8), 296-302. Retrieved from http://journal.ijierm.co.in/index.php/ijierm/article/view/2341
Section
Articles