AI-Driven Crop Rotation Planning System

Abstract

Agriculture plays a vital role in maintaining food security,economicgrowth,andenvironmentalsustainability.Tra-ditional farming practices often result in soil nutrient depletion, reducedcropproductivity,andincreaseddependenceonchemical fertilizers. Crop rotation is one of the most effective agricultural techniques used to improve soil fertility and maintain long-term agricultural productivity. This research presents an AI-driven crop rotation planning systemusingMachineLearningtechniques.Theproposedsystem analyzes soil nutrients, weather conditions, rainfall, humidity, and previous crop history to recommend suitable crop sequences for future farming seasons. The agricultural dataset undergoes preprocessing operations such as missing value handling, duplicate record removal, nor-malization, and categorical encoding before model training. A Random Forest Classifier algorithm is used for prediction due to its high accuracy, robustness, and ability to handle agricultural datasets effectively. AStreamlit-baseddashboardwasdevelopedtoprovideauser-friendly interface for farmers and researchers. The dashboard enables users to input agricultural parameters, generate crop rotation plans, and visualize soil nutrient changes graphically. Experimental results demonstrate that the proposed system provides intelligent and adaptive crop recommendations for precision agriculture and sustainable farming practices.

Introduction

Agriculture plays a vital role in food production and economic growth, but challenges such as population growth, climate change, soil degradation, and repeated cultivation of the same crops threaten long-term productivity. Traditional farming practices can reduce soil nutrients and increase pest and disease problems. Crop rotation is a sustainable solution that helps maintain soil fertility, improve nutrient availability, and enhance crop yield.

The proposed research develops an AI-based Crop Rotation Planning System using Machine Learning to recommend suitable crop sequences based on agricultural conditions. The system analyzes parameters such as soil nutrients (Nitrogen, Phosphorus, Potassium), temperature, humidity, rainfall, and previous crop history. A Random Forest Classifier is used because of its high accuracy, reliability, and ability to handle complex agricultural datasets. A Streamlit-based dashboard provides an interactive platform where users can enter farming parameters, receive crop recommendations, and visualize soil nutrient changes over multiple seasons.

Objectives

The main objectives of the system are:

• Analyze important agricultural parameters affecting crop growth.
• Clean and preprocess agricultural datasets for better prediction.
• Develop a Machine Learning-based crop recommendation model.
• Generate suitable crop rotation plans for future seasons.
• Visualize changes in soil nutrients over time.
• Support precision agriculture and sustainable farming.
• Reduce soil degradation and excessive fertilizer dependency.
• Provide a user-friendly dashboard for farmers and researchers.

Literature Review

Previous studies have shown that AI, Machine Learning, IoT, and data analytics can improve agricultural decision-making. Research on crop recommendation systems, deep learning, and smart farming demonstrates that AI can increase productivity and resource efficiency. However, existing systems mainly focus on crop prediction and often lack adaptive crop rotation planning and nutrient visualization. The proposed system addresses these limitations by combining crop recommendations with soil health analysis.

Methodology

The system follows these major steps:

1. Data Collection – Agricultural data including soil properties, weather conditions, and crop history are collected.
2. Data Cleaning and Preprocessing – Missing values, duplicate records, and inconsistent data are handled. Features are normalized and categorical data is encoded.
3. Feature Selection – Important factors such as N, P, K, temperature, humidity, rainfall, and previous crop history are selected.
4. Machine Learning Model Development – The Random Forest Classifier is trained for crop prediction.
5. Crop Rotation Recommendation – The model suggests suitable crop sequences according to soil and environmental conditions.
6. Dashboard Visualization – Results are displayed through a Streamlit interface with nutrient change graphs.

Machine Learning Model

The Random Forest Classifier was selected because it provides:

• High prediction accuracy
• Better handling of agricultural data
• Reduced overfitting
• Fast prediction performance
• Ability to process multiple input features
• Reliable decision-making under different farming conditions

The model predicts future crops by analyzing soil nutrients, weather conditions, and previous cultivation patterns.

System Implementation

The system was developed using:

• Python
• Scikit-learn
• Pandas
• NumPy
• Matplotlib
• Streamlit

The Streamlit dashboard allows users to:

• Enter soil and environmental parameters
• Select current crop details
• Generate crop rotation recommendations
• View graphical soil nutrient changes

The visualization feature helps farmers understand nutrient depletion trends and plan sustainable farming strategies.

Results

The developed system successfully generated dynamic crop rotation recommendations based on changing agricultural conditions. Example rotation output:

• Season 1: Wheat
• Season 2: Rice
• Season 3: Beans

The Random Forest model provided stable predictions, and the dashboard effectively displayed nutrient variations across farming seasons.

Applications

The system can be used in:

• Precision agriculture
• Smart farming
• Agricultural advisory services
• Sustainable farming practices
• Government agricultural programs
• Nutrient management systems
• Research and education

Advantages

• Intelligent crop recommendations
• Improved soil fertility management
• Reduced environmental impact
• Better agricultural productivity
• Automated decision support
• User-friendly interface
• Reduced fertilizer dependency
• Support for sustainable farming

Future Enhancements

Future improvements may include:

• IoT sensor integration
• Real-time weather data connection
• Deep learning-based prediction
• Mobile application development
• Cloud-based monitoring
• Multilingual support
• GPS-enabled smart farming
• AI chatbot assistance for farmers

Conclusion

ThisresearchpresentedanAI-drivencroprotationplanning system using Machine Learning techniques. The proposed system successfully predicted suitable crop rotationsequencesbasedonsoilnutrients,weatherconditions, and crop history. The Random Forest Classifier provided reliable prediction performance and adaptive crop recommendations.

References

[1] A. Kumar et al., “Machine Learning Techniques for Crop Recommen-dation Systems,” IEEE Access, vol. 10, pp. 22345–22360, 2023. [2] S. Patel and M. Verma, “Precision Agriculture using IoT and AI,”Springer Smart Farming Series, pp. 101–118, 2021. [3] J. Brown, “Crop Rotation and Sustainable Farming,” Agricultural Sci-ence Journal, vol. 18, no. 2, pp. 77–89, 2020. [4] R. Singh and P. Sharma, “Artificial Intelligence in Agriculture,” Inter-national Journal of Agricultural Technology, vol. 12, no. 3, pp. 45–52,2022. [5] M. Lee and K. Wong, “Deep Learning Approaches for Smart Agricul-ture,” Journal of AI Research, vol. 9, no. 4, pp. 210–224, 2021. [6] [6] L. Johnson and T. Miller, “Smart Farming Techniques using Machine Learning and Data Analytics,” International Journal of Smart Agriculture, vol. 15, no. 1, pp. 55–68, 2022. [7] P. Roy and S. Das, “IoT-Based Agricultural Monitoring and Crop Management Systems,” Journal of Agricultural Informatics, vol. 11, no. 3, pp. 120–134, 2021. [8] H. Kim and Y. Park, “Artificial Intelligence Applications in Precision Agriculture,” IEEE Transactions on Sustainable Computing, vol. 8, no. 2, pp. 98–112, 2023.

Copyright

Copyright © 2026 Jaydeep Koushik, Prof. Rakshitha B S. 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.