• Atharva Kadam
• S.S. Bhosale
• Aniket Alaspurkar
• Sumit Kamble
• S.N.Kamble, C.G.Patil

India is an agricultural country around 41% of Indian population works in farm and agro industries, contributing 20% of India’s total GDP. Due to unfavorable climate agricultural industry has been impacted largely and also cultivating same crop again and again in same field can lead to de-fertilization of land due to which farmers are unable to get better yield. To obtain maximum yield of best suitable crop, factors such as humidity, rainfall, temperature and soil along with its pH value can be considered to predict the best suitable crop. The main aim is to design a system using machine learning Algorithm implementing Random Forest to help the farmers in predicting the best suitable crop for their land. Random forest is ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time, for implementation of this System the database is available on Kaggle which will be used to implement this Algorithm, with accuracy of 92.84% in predicting the best crop for farmers. Random forest is the most popular and powerful supervised machine learning algorithm capable of performing both classification and regression tasks, that operate by constructing a multitude of decision trees during training time and generating output of the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Agriculture, Machine Learning, rop-prediction, Random Forest Algorithms, Crop yield, Kaggle

"MACHINE LEARNING BASED CROP PREDICTION SYSTEM FOR FARMERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.606-610, August-2022, Available :http://www.jetir.org/papers/JETIRFP06108.pdf

"MACHINE LEARNING BASED CROP PREDICTION SYSTEM FOR FARMERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp606-610, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06108.pdf