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AI Based Crop Farming with Crop Yield Prediction
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Farmers are the heart and soul of food security and have been at the forefront of the innovation required to adapt to modern problems something which by-itself serves as an example of a problem and a solution. They have been addressing the new food security issues such as climate change, soil erosion, and sub-optimal resource usage as best they can. One key to addressing these new problems is to accurately predict potential crop yields as such predictive analytics can be used to address in to use predictive analytics to address problems proactively. In this paper, we explain a multi-data source AI application which combines soil nutrient quality, environmental, and remote sensing index data, and cross engineered soil and environmental data with machine learning algorithms to predict the ANN, RF, and fuzzy logic. All data ranked and classified and missing data resolved to the appropriate confidence level and at the appropriate confidence level for data condition. Our proposed model, realized in Java/Weka has produced unparalleled optimum predictive analytics ratings. In the cross engineered environmental data weighted model, banana cultivation was predicted to have 95.83% optimum yield, predicted precision 85.71% with a confidence of 90% and an F1 of 88 predictive F1 to 50.18 tons/ha at a confidence of 98.06%, R². Predictive analytics are self-optimizing and will increase the predictive yield as necessitated that will increase crop yield with decreased added irrigation by remaining crops.
Keywords
AI, Machine Learning, Crop Yield Prediction, Smart Farming, ANN, Random Forest.
References
[1] Mr. Telise Vinod,N. Chandrasena Reddy,Rapolu Suresh Reddy,Anugu Athrey Reddy.” Crop Yield Prediction using Machine Learning” International Journal of Engineering Research & Technology (IJERT) IJERTV12IS040077 Vol. 12 Issue 04, April-2023.
[2] Shafiulla Shariff,Shwetha R B,Ramya O G,Pushpa H,Pooja K.” Crop Recommendation using Machine Learning Techniques” International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 ICEI – 2022 Conference Proceedings Special Issue – 2022
[3] Prameya R Hegde, Ashok Kumar A R . ”Crop Yield and Price Prediction System for Agriculture Application” International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 IJERTV11IS070060 Vol. 11 Issue 07, July-2022
[4] Bali, Nishu, and Anshu Singla.” Emerging trends in machine learning to predict crop yield and study its influential factors: A survey.” Archives of computational methods in engineering (2022): 1-18.
[5] Gunjan, Vinit Kumar, et al.” Prediction of agriculture yields using machine learning algorithms.” Pro- ceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications: ICMISC 2021. Springer Singapore, 2022.
[6] Reddy, D. Jayanarayana, and M. Rudra Kumar. ”Crop yield prediction using machine learning algo- rithm.” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021.
[7] Agarwal, Sonal, and Sandhya Tarar. ”A hybrid approach for crop yield prediction using machine learning and deep learning algorithms.” Journal of Physics: Conference Series. Vol. 1714. No. 1. IOP Publishing, 2021.
[8] Morales, Alejandro, and Francisco J. Villalobos. “Using machine learning for crop yield prediction in the past or the future.” Frontiers in Plant Science 14 (2023): 1128388.
[9] Joshua, S. Vinson, et al. “Crop yield prediction using machine learning approaches on a wide spectrum.” Computers, Materials & Continua 72.3 (2022).
[10] Senapaty, Murali Krishna, Abhishek Ray, and Neelamadhab Padhy. “IoT-enabled soil nutrient analysis and crop recommendation model for precision agriculture.” Computers 12.3 (2023): 61.
How to cite this paper
@article{1715479,
author = {Prof. Kirti Deore, Shravani Nitin Nanekar, Pranita Prashant Kharat, Disha Navnath Bhuite, Nikita Rajaram Nikam},
title = {AI Based Crop Farming with Crop Yield Prediction},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {2223-2228},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715479.pdf},
abstract = {Farmers are the heart and soul of food security and have been at the forefront of the innovation required to adapt to modern problems something which by-itself serves as an example of a problem and a solution. They have been addressing the new food security issues such as climate change, soil erosion, and sub-optimal resource usage as best they can. One key to addressing these new problems is to accurately predict potential crop yields as such predictive analytics can be used to address in to use predictive analytics to address problems proactively. In this paper, we explain a multi-data source AI application which combines soil nutrient quality, environmental, and remote sensing index data, and cross engineered soil and environmental data with machine learning algorithms to predict the ANN, RF, and fuzzy logic. All data ranked and classified and missing data resolved to the appropriate confidence level and at the appropriate confidence level for data condition. Our proposed model, realized in Java/Weka has produced unparalleled optimum predictive analytics ratings. In the cross engineered environmental data weighted model, banana cultivation was predicted to have 95.83% optimum yield, predicted precision 85.71% with a confidence of 90% and an F1 of 88 predictive F1 to 50.18 tons/ha at a confidence of 98.06%, R². Predictive analytics are self-optimizing and will increase the predictive yield as necessitated that will increase crop yield with decreased added irrigation by remaining crops.},
keywords = {AI, Machine Learning, Crop Yield Prediction, Smart Farming, ANN, Random Forest.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715479}
}