Smart Agriculture Using Artificial Intelligence

Abstract

This research leverages Artificial Intelligence (AI) techniques such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest to enhance agricultural productivity and sustainability. By integrating AI with sensor and image data, the study focuses on intelligent water crop irrigation, crop disease detection, and crop disease prevention. The irrigation module employs machine learning models to analyze soil moisture, temperature, and humidity data for efficient water management. Simultaneously, deep learning-based image classification using CNN enables accurate identification of plant diseases from leaf images, facilitating timely preventive actions. The system also provides real-time recommendations for disease control and optimal irrigation schedules. Through this AI-driven approach, farmers can make data-informed decisions, reduce resource wastage, and improve crop yield. The integration of AI in agriculture demonstrates a step forward toward smart, automated, and sustainable farming practices.

Introduction

The agricultural sector is evolving rapidly with the integration of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) to address challenges like climate change, water scarcity, and crop diseases. Traditional farming methods are becoming less effective, leading to the adoption of AI-powered smart farming systems for enhanced productivity, efficiency, and sustainability.

These systems use sensors, drones, and satellite imagery to collect real-time data on soil, weather, and crop health. AI models process this data to:

• Predict crop yield

• Detect diseases early

• Optimize irrigation

• Automate decision-making

Key Technologies and Models Used:

• Support Vector Machine (SVM), Random Forest, CNN, KNN, and LSTM are applied for tasks such as:

  • Disease detection

  • Irrigation management

  • Crop prediction

Literature Review Highlights:

• SVM and Random Forest achieved ~80–85% accuracy in disease and irrigation prediction.

• CNN models reached ~95% accuracy in leaf disease detection.

• Hybrid models like CNN + LSTM improved performance to ~92%, especially for time-series crop and weather data.

Methodology:

• Data from IoT sensors (e.g., soil moisture, humidity, temperature) and image datasets is preprocessed (cleaning, normalization, enhancement).

• ML algorithms analyze this data to predict water needs and detect crop diseases.

• Evaluation is done using accuracy, precision, recall, and F1-score.

Experimental Results:

• CNN emerged as the top performer with ~90% accuracy.

• SVM: ~85%

• Random Forest: ~80%

• KNN: ~82%

• LSTM model achieved ~88% accuracy (based on confusion matrix analysis).

• The system accurately classified crops as healthy, diseased, or needing irrigation.

Conclusion

This study presented an AI-driven approach to enhance agricultural practices through the integration of technologies such as crop disease detection, intelligent irrigation management, and crop health monitoring. The methodology involved data collection from sensors, drones, and satellite imagery, followed by preprocessing and analysis using machine learning and deep learning models. Experimental results demonstrated that while traditional models like Random Forest and Support Vector Machine (SVM) performed efficiently for structured sensor data, deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) achieved higher accuracy in detecting crop diseases and predicting irrigation needs due to their ability to capture complex spatial and temporal patterns. The results confirm that AI techniques can significantly improve decision-making in agriculture by optimizing water usage, reducing crop losses, and increasing productivity. Although deep learning models require more computational resources, their superior accuracy makes them ideal for real-time agricultural monitoring systems. Future work can explore the use of advanced AI architectures like transformers and federated learning for privacy-preserving data analysis, as well as integrating IoT-based smart sensors for continuous field monitoring. Overall, this research demonstrates that the fusion of Artificial Intelligence with agriculture provides a sustainable, data-driven framework for modern farming—empowering farmers to make informed decisions, conserve resources, and achieve higher yields efficiently.

References

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Copyright

Copyright © 2025 Pratiksha Nanavare, Saurabh More, Nandini Bung, Harsh Kamble, Prof. Mrs. Dhanashri Dixit, Prof. Ms. Bhagyashree Patil. 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.