Authors: Arya Phadnis, Shivam Panchal, Rajat Jadhav, Buddhi Rajdeep, Deepak Patil
Certificate: View Certificate
Crop prediction is the process of forecasting the yield or production of crops for a given period, based on historical data, weather patterns, and other relevant factors. The prediction can be used to inform decisions regarding planting, harvesting, and marketing of crops. Machine learning and artificial intelligence techniques are increasingly being used to improve crop prediction accuracy. These techniques use algorithms to analyze large amounts of data, such as weather patterns, soil conditions, and crop history, to make predictions about future crop yields. Crop prediction models can be used by farmers, agribusinesses, and governments to optimize crop management, reduce waste, and maximize profits. Accurate crop prediction can also help to mitigate the impact of climate change on agricultural production by enabling farmers to adapt to changing weather patterns and other environmental factors
Crop prediction using machine learning is an application of artificial intelligence that enables farmers to make more informed decisions about crop management. It involves the use of historical data on weather conditions, soil quality, and crop yields to create predictive models that can forecast 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 forecast future crop yields. The use of machine learning for crop prediction has several benefits. Farmers can make more informed decisions about crop management, such as the best time to plant, fertilize, or irrigate crops. Predictive models can also help farmers estimate future crop yields and plan for harvest and storage accordingly. Machine learning can also be used to identify early signs of crop disease or pests. By analyzing historical data on pest and disease outbreaks, machine learning algorithms can identify potential risks and alert farmers to take preventive measures.
In summary, crop prediction using machine learning 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 decisions about crop management and identify potential risks before they become major problems.
II. LITERATURE SURVEY
The architecture for crop prediction using machine learning (ML) generally follows a similar pattern, consisting of the following components:
Overall, the architecture for crop prediction using ML involves collecting and preprocessing data, selecting relevant features, selecting and training a suitable ML model, evaluating the model's performance, and making predictions 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 at hand.
Note: This architecture is a general overview of the process and may vary based on the specific requirements and constraints of the crop prediction system.
IV. BLOCK DIAGRAM
As in Fig.1, below is the in-general process of how the prediction of the crop is done using Machine Learning:
In conclusion, using machine learning for crop prediction has shown promising results in recent years. By utilizing 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 especially useful for large-scale farming operations where the timely and accurate prediction of crop yield and potential issues can significantly impact productivity and profitability. However, it is important to note that machine learning models require large amounts of data to train and optimize the algorithms. Additionally, the quality and accuracy of the data used can greatly affect the performance of the models. Therefore, it is essential to ensure the quality and accuracy of the data used in crop prediction to obtain reliable and useful results. Overall, using machine learning in crop prediction can revolutionize the agriculture industry by providing accurate and timely information to farmers, allowing for better planning and decision-making.
 Crop Yield Analysis Using Machine Learning Algorithms by Fatin Farhan Haque, Ahmed Abdelgawad, Venkata Prasanth Yanambaka, Kumar Yelamarthi (2020).  Crop Yield Prediction using Machine Learning Techniques by Ramesh Medar, Vijay Rajpurohit, Shweta (2019)  Crop Yield Prediction using Machine Learning Techniques by Ramesh Medar, Vijay Rajpurohit, Shweta (2019).  A Study on Various Data Mining Techniques for Crop Yield Prediction by Yogesh Gandge, Sandhya (2017).  Crop prediction using predictive analytics by P. S. Vijayabaskar, Sreemathi R, Keertanaa E (2017).  Supervised Classification of Spectral Signatures from Agricultural Land-Cover in Panama Using the Spectral Angle Mapper Algorithm by Javier E. Sanchez-Gal (2019).  Supervised Machine learning Approach for Crop Yield Prediction in Agriculture Sector by DR. Y. Jeevan, Nagendra Kumar (2022).  Crop yield prediction using machine learning by Mayank Champaneri, Chaitanya Chandvidkar, Darpan Chachpara, and Mansing Rathod (2020).  Crop yield prediction using Deep Neural Networks by Saeed Khaki, and Lizhi Wang (2019).
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.