Precision Farming – A Machine Learning Based Crop Recommender

Precision Farming – A Machine Learning Based Crop Recommender

Dr. K. Madan Mohan1, M. Rochani Reddy2, M. Mani Kumar3, G. Ravi Teja4

1Associate Professor, Department of Computer Science & Engineering, Guru Nanak Institute of Technology

2,3,4Student, Department of Computer Science & Engineering, Guru Nanak Institute of Technology

ABSTRACT: In rural India, agriculture and its related industries are without a doubt the main sources of income. The nation's Gross Domestic Product (GDP) is also significantly influenced by the agriculture sector. The vast scale of the agriculture industry is a blessing for the nation. The yield per acre of crops, however, is disappointing when compared to global norms. This is among the potential reasons why India's marginal farmers commit suicide at a higher rate. A practical and easy-to-use yield forecast method for farmers is suggested in this research. Farmers can connect to the planned system through a smartphone application. GPS aids in determining the user's location. The user enters the soil type and region. Predicting the crop yield for a user-selected crop or selecting the most lucrative crop list are made possible by machine learning algorithms. K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multivariate Linear Regression (MLR) are some of the machine learning methods used to forecast agricultural productivity. With a 95% accuracy rate, the Random Forest produced the best results out of all of them. In order to increase yield, the system also recommends the optimal time to apply fertilizers.

Key Words: Precision Farming, Machine Learning, Crop Recommendation, Farm Data Analysis, Soil Quality Assessment