Authors: Arya Phadnis, Shivam Panchal, Rajat Jadhav, Buddhi Rajdeep, Deepak Patil
DOI Link: https://doi.org/10.22214/ijraset.2023.52265
Certificate: View Certificate
Crop forecasting is the process of predicting the yield or production of crops for a given period based on historical data, weather and other relevant factors. The prediction can be used to inform crop planting, harvesting and marketing decisions. Machine learning and artificial intelligence techniques are increasingly being used to improve the accuracy of crop forecasting. These techniques use algorithms to analyze large amounts of data, such as weather patterns, soil conditions, and crop history, to predict future crop yields. Crop prediction models can be used by farmers, agribusinesses, and governments to optimize crop management, reduce waste and maximize profit. Accurate crop forecasting can also help mitigate the impact of climate change on agricultural production by enabling farmers to adapt to changing weather conditions and other environmental factors.
I. INTRODUCTION
Machine learning crop prediction is an application of artificial intelligence that enables farmers to make more informed crop management decisions. It involves using historical data on weather conditions, soil quality and crop yields to create predictive models that can predict future crop yields.
Machine learning algorithms such as Random Forest, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be used to predict crop yields. These algorithms analyze historical data and identify patterns that can be used to predict future crop yields. Using machine learning for crop prediction has several advantages. Farmers can make more informed crop management decisions, such as the best time to plant, fertilize or irrigate crops. Predictive models can also help farmers estimate future crop yields and plan harvest and storage accordingly. Machine learning can also be used to identify early signs of crop diseases or pests. By analyzing historical data on pest and disease outbreaks, machine learning algorithms can identify potential risks and alert farmers to take preventative measures.
In summary, machine learning crop prediction is a valuable tool for farmers to optimize crop management and maximize crop yields. By using historical data to create predictive models, farmers can make more informed crop management decisions and identify potential risks before they become major problems.
II. LITERATURE SURVEY
III. EXISTING SYSTEM
IV. PROPOSED SYSTEM
V. ARCHITECTURE
The architecture for machine learning (ML) crop prediction generally follows a similar pattern, consisting of the following components:
Overall, the architecture for ML crop prediction includes data collection and preprocessing, selection of relevant features, selection and training of an appropriate ML model, evaluation of model performance, and prediction on new data.
This architecture provides a general overview of the steps involved in using machine learning for crop prediction. The specific details and requirements of each step may vary depending on the specific problem.
Note: This architecture is a general overview of the process and may vary depending on the specific requirements and constraints of the crop prediction system.
VII. ALGORITHM REQUIRED
SVM Algorithm:-Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. Although we call regression problems, they are best suited for classification. The goal of the SVM algorithm is to find a hyperplane in N-dimensional space that distinctly classifies the data points. SVMs are used in applications such as handwriting recognition, intrusion detection, face detection, email classification, gene classification, and web pages. This is one of the reasons why we use SVM in machine learning. It can process both classification and regression on linear and non-linear data.
VIII. RESULT ANALYSIS
In conclusion, the use of machine learning for crop prediction has shown promising results in recent years. Using various techniques such as data analysis, statistical modeling, and pattern recognition, machine learning algorithms can accurately predict crop yield, disease outbreaks, and optimal harvest times. This technology is particularly useful for large-scale farming operations, where timely and accurate forecasting of crop yield and potential problems can significantly impact productivity and profitability. However, it is important to note that machine learning models require a large amount of data to train and optimize the algorithms. In addition, the quality and accuracy of the data used can significantly affect the performance of the models. Therefore, it is essential to ensure the quality and accuracy of the data used in crop prediction in order to obtain reliable and useful results. Overall, the use of machine learning in crop prediction can revolutionize agriculture by providing farmers with accurate and timely information, enabling better planning and decision making.
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Copyright © 2023 Arya Phadnis, Shivam Panchal, Rajat Jadhav, Buddhi Rajdeep, Deepak Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET52265
Publish Date : 2023-05-14
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here