AI Based Crop Yield Prediction: A Review

Title

AI Based Crop Yield Prediction: A Review

Author

1. Harshal Borate, Student, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology,Baramati,Pune, India
2. shital Sapkal, Student, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology,Baramati,Pune, India
3. Priya Taware, Student, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology,Baramati,Pune, India
4. Jyoti Rangole, Professor, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology,Baramati,Pune, India

Abstract

The current stage of technological advancement is being widely applied to development internationally. The Artificial Intelligence (AI) application to intelligent farming, which generates crops in agriculture, is one of the technological advancements. India's economy depends largely on agriculture because humans are the source of all of its resources. The primary impediment to food security is population expansion, which raises food demand. To raise the supply, farmers must yield more on the same quantity of land. Technology can help farmers enhance their productivity by anticipating crop yields. This study's primary purpose is to forecast agricultural yield by using the factors of precipitation, crop, weather, region, yield, moreover production things provided a big danger

Keywords

Conclusion

The agricultural industry has undergone a huge upheaval thanks in great part to precision agriculture. Using AI models and wireless sensor networks for weed identification and crop prediction, as part of our suggested model, has also helped to boost agricultural output. Modern agricultural strategies that use AI and IoT principles will undoubtedly benefit farmers globally in making better judgments and in boosting the yield and efficiency of their crops overall. This is not the case with traditional agricultural processes, which take longer, involve more labor, and may provide erroneous results or losses. The suggested model has the ability to precisely govern the agricultural industry

Author Contrubution

All authors contributed to the design, implementation, and evaluation of the crop yield prediction model. Shital sapkal led the data collection, preprocessing, and model training. All authors contributed to the final manuscript writing and approved the submitted version.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.

Data Sharing Statement

The dataset used in this study was obtained from publicly available sources such as  Kaggle. The processed dataset and the model code are available upon request for academic and non-commercial purposes.

Software And Tools Use

The model was developed using Python, with libraries such as Pandas, NumPy, Scikit-learn, and Flask. Data visualization was done using Matplotlib and Seaborn. Jupyter Notebook was used for implementation and testing

Acknowledgements

The authors thank Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology,Baramati for providing the computational resources and support. Special thanks to Dr.J.S.Rangole mam for valuable feedback on the model design.