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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62453
Volume:
10 |
5 |
May 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer science
Sushant University
Computer science
Sushant University

Computer science
Sushant University
Annadata AI is an intelligent agriculture system that uses machine learning to assist farmers in selecting the most suitable crop based on soil and environmental conditions. The system takes input parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH, and rainfall to generate accurate crop recommendations. Different machine learning models, including Decision Tree, Random Forest, and Neural Network, were implemented and compared. The results show that the Random Forest model achieved the highest accuracy of 99.2%, making it the most reliable model for prediction. The system is developed as a web application using Flask, making it easy to use for farmers. It also includes additional features such as disease detection and chatbot assistance. The project aims to promote smart farming, improve productivity, and reduce risks in agriculture through data-driven decision-making.
KEYWORDS
The Annadata AI system has hit the mark by creating a smart way to recommend crops to farmers using machine learning. It looks at things like the soil and the environment, and then helps farmers pick the best crop to plant. This means they can get a better harvest and lower their risks. By doing this, the system is making farming easier and more efficient, which is a big deal for farmers who want to get the most out of their land. Among all models, Random Forest showed the highest accuracy and was selected as the final model. The system is easy to use and provides quick and reliable results through a web interface. This project highlights the importance of AI in agriculture and shows how technology can improve farming practices
References Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2016). TensorFlow: Large-scale machine learning on heterogeneous systems. Retrieved from https://www.tensorflow.org Grinberg, M. (2018). Flask web development: Developing web applications with Python. O’Reilly Media. IEEE. (2024). Machine learning applications in agriculture systems. IEEE Xplore. Retrieved from https://ieeexplore.ieee.org StartUs Insights. (2024). Precision agriculture trends 2025. Retrieved from https://www.startus-insights.com Singh, A., Kumar, R., & Sharma, P. (2025). Crop recommendation using machine learning techniques. Scientific Reports.
Publication Date
5/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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