InteliCrop: An Ensemble Model to Predict Crop using Machine Learning Algorithms
IEEE BASE PAPER ABSTRACT:
The agricultural sector is the prime occupation of India. Researchers are developed various scientific technology in the agriculture field for better yield. In this paper, we try to form an ensemble model using various machine learning algorithms for better rice production. Crop production prediction utilizing AI Strategies aims to deliver improved outcomes, but the ensemble model provides better predictive results compared to the individual algorithm. We tried to use a combination of symmetric machine learning algorithms to form an ensemble model for better prediction. Here symmetric algorithms such as random forest, Gradient Boosting, and Logistic Regression are individually used for the prediction of the yield of rice. While combining all the aforesaid algorithms to form an ensemble model of ers a better result (99.54%).
OUR PROPOSED ABSTRACT:
Agriculture and industries closely related to it are the main sources of income for the people in India. Comparing current years to earlier ones, agriculture has never been in good shape. Lack of a well-organized farming pattern and appropriate instructions to the farmers is a key factor in this. The country’s economy heavily depends on agriculture. In addition to this, India suffers from natural catastrophes like floods and droughts, which destroy the crops. When harvesting crops, one’s approach should be spot on; aspects like temperature, humidity, and rainfall should be well planned, as well as when to harvest the crop to receive the greatest yield. In order to make numerous policy decisions regarding crop production to assure food availability, early crop output projection before harvest is crucial. Conventional methods rely on pricey, non-scalable survey data, and results are typically only made public after harvest. This study presents an ensemble model for crop prediction that uses three machine learning algorithms, namely Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier. The proposed model employs the VotingClassifier ensemble method to combine the outputs of the individual algorithms and make more accurate predictions. The dataset used in the study contains agricultural features such as temperature, rainfall, soil quality, and fertilization. The results indicate that the ensemble model achieves higher accuracy rates than the individual algorithms, indicating the efficacy of the proposed approach. The study highlights the potential of machine learning in agricultural prediction and provides a practical solution for crop yield estimation. The proposed ensemble model can be useful for farmers and agricultural researchers to make informed decisions about crop management and optimization.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Ensemble Model (VotingClassifier). Used three algorithm
1) Random Forest Classifier,
2) Logistic Regression,
3) Gradient Boosting Classifier.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Muppala Sunny Chowdhary, Jacob Jasper Roy Paga, Meera Gandhi, Sasmita S. Choudhury and SachiNandan Mohanty, “InteliCrop: An Ensemble Model to Predict Crop using Machine Learning Algorithms”, IEEE Conference, 2022.
Tag:best python projects, deep learning projects, deep learning projects for final year, ieee papers on python projects, ieee projects, ieee projects for cse, ieee projects for cse in python, machine learning projects, machine learning projects for final year, ml projects, python ai projects, python ieee projects, python ieee projects in machine learning, python machine learning projects