Smart Crop Recommendation Using Machine Learning for Precision Agriculture

Smart Crop Recommendation Using Machine Learning for Precision Agriculture

1Ramathirtham Vaishnavi ,2Tejavath Sravya,3Shaik Vaseem , 4Dr. K. Madan Mohan

1,2,3UG Scholars, 4Associate Professor

4drmohan.csegnit@gniindia.org

1,2,3,4Department of Computer Science and Engineering

1,2,3,4Guru Nanak Institute of Technology, Hyderabad, Telangana, India.

Abstract - Agriculture and its allied sectors are undoubtedly the largest providers of livelihoods in rural India. The agriculture sector is also a significant contributor factor to the country's Gross Domestic Product (GDP). Blessing to the country is the overwhelming size of the agricultural sector. However, regrettable is the yield per hectare of crops in comparison to international standards. This is one of the possible causes for a higher suicide rate among marginal farmers in India. This paper proposes a viable and user-friendly yield prediction system for the farmers. The proposed system provides connectivity to farmers via a mobile application. GPS helps to identify the user location. The user provides the area & soil type as input. Machine learning algorithms allow choosing the most profitable crop list or predicting the crop yield for a user selected crop. To predict the crop yield, selected Machine Learning algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Multivariate Linear Regression (MLR), and K-Nearest Neighbour (KNN) are used. Among them, the Random Forest showed the best results with 95% accuracy. Additionally, the system also suggests the best time to use the fertilizers to boost up the yield.

Key Words:  Machine Learning, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Multivariate Linear Regression (MLR), K-Nearest Neighbour (KNN)