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Light Weight CNN based Robust Image Watermarking Scheme for Security
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Principle of 6G Wireless Networks: Vision, Challenges and Applications
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PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
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Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
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Augmented Reality in Education
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A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
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IoT based Biotelemetry for Smart Health Care Monitoring System
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Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
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GUI based Industrial Monitoring and Control System
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AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3
Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
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Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 4 | Issue - 3 | september 2022
Published
16 September, 2022
Agriculture is the main livelihood in India. Most of the people earn bread and butter through farming, but the farmers are not getting enough profit and the field is facing growth downward due to irregular rainfall, high volatility in agriculture commodity prices and uncertainties in production. The objective of this study is to design and implement an automated crop price prediction system with best suitable machine learning technique, as well as displaying prediction results on website Krishi-Stats designed for easy understanding for Farmers. In this study, three machine-learning (ML) algorithms, ARIMA, VAR and XGBoost are applied on large historical data collected from government website. The ML algorithms compared with their root mean square error values (RMSE). As XGBoost has given optimum RMSE value of 0.94, has been selected as the prediction system engine of our website Krishi-Stats. On website, the crop prediction prices are plotted for all twelve selected crops and visualized using prediction graphs.
KeywordsPrecision agriculture crop price prediction machine learning XGBoost ARIMA VAR.
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