Performance Evaluation of Machine Learning Techniques for Mustard Crop Yield Prediction from Soil Analysis

2020 ◽  
Vol 64 (02) ◽  
pp. 394-398
Author(s):  
Vaishali Pandith ◽  
Haneet Kour ◽  
Surjeet Singh ◽  
Jatinder Manhas ◽  
Vinod Sharma
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Crop Yield ◽  
Soil Analysis ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Learning Techniques ◽  
Mustard Crop

scholarly journals ML Methods for Crop Yield Prediction and Estimation: An Exploration

2020 ◽  
Vol 9 (3) ◽  
pp. 3506-3508
Keyword(s):  
Machine Learning ◽  
Detailed Analysis ◽  
Crop Yield ◽  
Learning Process ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Yield Estimation ◽  
Data Set ◽  
Yield Optimization ◽  
Learning Techniques

Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

scholarly journals Experimental Analysis of Machine Learning Algorithms Based on Agricultural Dataset for Improving Crop Yield Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 3246-3251
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Bayes Net ◽  
Learning Techniques ◽  
Production Area ◽  
Primary Research Study

Agriculture is the primary research study area in India as agriculture is the main source of income for various communities. In classification algorithm for agricultural dataset according to production, area, crop and seasons. Here, four classification algorithms are used with the help of WEKA tool. These algorithms are namely the present scenario, there is a call to renovate the enormous agriculture data into diverse technologies and make them accessible to the farmer for improved decision making. The endeavor of this work is to find out the finest Random Tree, J48, Bayes Net and KStar etc. The captured results revealed that Random tree algorithm performed well in terms of error rate and provides slightly better performance than KStar, Bayes Net and J48 classifiers. In this paper, our objective is to apply machine learning techniques to mine constructive information from the agricultural dataset to improve the crop yield prediction for major crops in Nashik district of Maharashtra.

scholarly journals Crop Yield Prediction Using Machine Learning Techniques

2020 ◽  
pp. 312-315
Author(s):  
Ashwini I. Patil ◽  
Ramesh A. Medar ◽  
Vinod Desai
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Model Performance ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
The People ◽  
Learning Techniques ◽  
High Production ◽  
Day By Day

Today Indian economy depends upon agriculture. More than 70% of the people in India have taken it as a main occupation, day by day for a particular crop; the formers are not getting proper yield as well as profit due to environmental conditions like soil quality, weather, heavy rainfall, drought, seed damages, fertilizers, pesticides. The farmers not able to produce high production, so taking the historical agricultural data records we can predict the crop yield using machine learning techniques like Linear regression, comparative analysis are done with decision tree, KNN algorithms, using these to achieve the high accuracy and model performance is computed.

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