Crop recommendation and yield prediction using machine learning algorithms
Department of Computer Science and Engineering, Meenakshi Sundararajan Engineering College, Anna University, Chennai, India.
Review Article
World Journal of Advanced Research and Reviews, 2022, 14(03), 452–459
Publication history:
Received on 13 May 2022; revised on 16 June 2022; accepted on 18 June 2022
Abstract:
Agriculture is the foundation of many countries' economies, particularly in India and Tamil Nadu. The young generation who are new to farming may confront the challenge of not understanding what to sow and what to reap benefit from. This is a problem that has to be addressed, and it is one that we are addressing. Predicting the proper crop and production will aid in making better decisions, reducing losses and managing the risk of price fluctuations. The existing system is not deployed, unlike ours, which is done by applying classification and regression algorithms to calculate crop type recommendations and yield predictions. Agricultural industries must use machine learning algorithms to anticipate the crop from a given dataset. The supervised machine learning technique is used to analyse a dataset in order to capture information from multiple sources, such as variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments, and so on. A comparison of machine learning algorithms was conducted in order to identify which algorithm was more accurate in predicting the best harvest. The results show that the proposed machine learning algorithm technique has the best accuracy when comparing entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity, and Entropy.
We have ensured that our proposed system accomplishes its job effectively by projecting the yield of practically all types of crops grown in Tamil Nadu, relieving some of the burden from their shoulders as they enter a new business.
Keywords:
Supervised Machine Learning Approach; Classification and Regression Models; Precision; Linear Regression; Logistic Regression; Decision Tree and Random Forest
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