Smart Agriculture Based on Weather and Crop Prediction Using Edge and Cloud Computing

Abstract

The pressure to feed the growing global population is only growing. Farmers now have to balance the need to safeguard the environment, water scarcity, and climate change with growing more food. Due to its slowness, resource waste, and excessive reliance on guesswork, traditional farming simply cannot keep up. The integration of IoT with edge and cloud-based computing can help with that. The field gains some real intelligence from this mix. In this study, we examine a smart agriculture system that integrates cloud and edge innovations. It keeps the entire operation operating autonomously while enabling farmers to sense what\'s happening in the moment, process data quickly, and even predict what\'s coming next. Edge devices make snap decisions and perform calculations involving mathematics on decisions and crunch numbers. While the cloud stores huge quantities of data, runs in-depth analyses, and guides long-term planning, edge devices make snap decisions and crunch numbers. As a result, the system operates smoothly, crops grow better, and less water and fertilizer are used. According to recent experiments, this approach greatly boosts efficiency in agriculture and improves sustainability overall. We finish by looking at the other obstacles and possible futures of smart farming technology.

Introduction

Agriculture Challenges:
Modern farmers face unpredictable weather, limited water, soil degradation, labor shortages, and rising costs. With the global population expected to increase substantially, food production must rise by nearly 70% by 2050. Traditional farming methods are reactive and inefficient, often resulting in suboptimal yields.

Technological Shift:
The integration of IoT sensors, edge computing, and cloud-based analytics enables real-time monitoring and intelligent decision-making, transforming agriculture from reactive to precision-based. Edge devices process data locally for fast responses, while cloud platforms provide high-performance computation, big data analysis, and machine learning for long-term strategic insights.

1. Literature Review Highlights

• Edge-based monitoring: Reduces delays, improves irrigation accuracy (Shi et al.)

• Cloud-based deep learning: Early crop disease detection (Zhang et al.)

• Hybrid edge-cloud systems: Optimize irrigation, save water and energy (Li et al.)

• Distributed smart farming platforms: Integrate IoT, edge, and cloud for large-scale automation (Patil & Kulkarni)

Insight: Combining edge and cloud computing enhances responsiveness, reliability, and scalability of smart agriculture systems.

2. System Architecture

A smart agriculture system consists of four layers:

1. Sensing Layer:

   • IoT sensors measure soil moisture, air temperature & humidity, soil pH, nutrient levels, light intensity, and capture crop images.

   • Enables continuous environmental and crop monitoring.

2. Edge Processing Layer:

   • Local devices (IoT gateways, microcomputers) filter, preprocess, and analyze data.

   • Perform real-time anomaly detection and trigger local actions (irrigation, fertilization, pest control).

3. Cloud Intelligence Layer:

   • High-performance computing for big data storage, machine learning, predictive analytics, climate forecasting, and crop yield estimation.

   • Supports long-term decision-making and strategic farm planning.

4. Application Layer:

   • User-friendly interfaces (mobile apps, web dashboards) provide real-time alerts, AI-driven recommendations, and actionable insights.

3. Smart Intelligence Features

• Crop Disease Identification: Deep learning models detect early signs of disease and nutrient deficiencies for timely intervention.

• Intelligent Irrigation Management: Algorithms analyze soil, weather, and crop data to optimize irrigation schedules, reducing water usage and improving growth.

• Yield Prediction & Planning: Regression and neural network models forecast expected yield for better resource and supply chain management.

4. Operational Workflow

1. System Initialization: Activate sensors, edge devices, actuators, and cloud services.

2. Data Collection: Continuous measurement of soil, weather, and crop conditions.

3. Edge Preprocessing: Filter noise, normalize data, extract features, handle missing values.

4. Real-Time Edge AI: Detect anomalies like low moisture, temperature stress, or disease.

5. Local Actuation: Trigger irrigation, fertilizer, pest control, or climate adjustments.

6. Cloud Analytics: Perform advanced predictive analytics and model updates.

7. Farmer Notifications: Deliver actionable recommendations via app, web, or SMS.

8. Feedback Loop: Update AI models and optimize control policies.

9. Continuous Monitoring: Cycle repeats for real-time adaptive management.

Key Benefit: Combines real-time local decision-making with long-term cloud intelligence, enabling precise, efficient, and sustainable agriculture.

Takeaway: Smart agriculture systems integrating IoT, edge, and cloud computing provide farmers with fast, data-driven responses to crop stress, optimize resource use, and support sustainable, high-yield farming practices.

Conclusion

Edge–cloud enabled smart agriculture represents a transformative approach toward intelligent, sustainable, and precision farming. By combining real-time sensing, low-latency edge analytics, and cloud-based artificial intelligence, farmers can achieve optimized productivity, efficient resource utilization, and reduced environmental impact. Continued research and technological advancements will further accelerate the global adoption of intelligent farming systems.

References

[1] FAO, The Future of Food and Agriculture: Trends and Challenges, United Nations, 2023. [2] Satyanarayanan, M., Edge Computing: Vision and Challenges, IEEE Computer, 2022. [3] Shi, W., et al., Real-Time Edge Analytics for Smart Agriculture, IEEE Access, 2023. [4] Zhang, Y., et al., Cloud-Based Deep Learning for Crop Disease Detection, Computers and Electronics in Agriculture, 2024. [5] Li, X., et al., Hybrid Edge–Cloud Framework for Intelligent Irrigation, Future Generation Computer Systems, 2024. [6] Patil, K., Kulkarni, R., IoT-Based Smart Farming Architecture, International Journal of Agricultural Informatics, 2023. [7] Gubbi, J., et al., Internet of Things: A Survey, Future Generation Computer Systems, 2022. [8] Satyanarayanan, M., Edge Analytics for Low-Latency Applications, IEEE Internet Computing, 2023. [9] Amazon Web Services, Cloud Infrastructure for Smart Agriculture, White Paper, 2024. [10] Mohanty, S. P., et al., Deep Learning for Plant Disease Detection, Computers and Electronics in Agriculture, 2023. [11] Jeong, J. H., et al., Machine Learning-Based Crop Yield Prediction, Agricultural Systems, 2024. [12] Cisco, IoT and Edge Architecture for Smart Farming, Technical Report, 2023. [13] Satyanarayanan, M., Edge Computing: Vision and Challenges, IEEE Computer, 2022. [14] Shi, W., et al., Edge Computing for Smart Agriculture, IEEE Access, 2023. [15] Gubbi, J., et al., Internet of Things: A Vision and Future Directions, Future Generation Computer Systems, 2022. [16] Mekki, K., et al., A Comparative Study of LPWAN Technologies for IoT, ICT Express, 2023. [17] Li, X., et al., Hybrid Edge–Cloud Architecture for Precision Agriculture, Future Generation Computer Systems, 2024. [18] Zhang, Y., et al., Edge-Based Anomaly Detection for Agricultural IoT Systems, IEEE Internet of Things Journal, 2024. [19] Patil, K., Kulkarni, R., Smart Irrigation Using Edge Computing and IoT, International Journal of Agricultural Informatics, 2023. [20] Mohanty, S. P., et al., Deep Learning for Smart Agriculture, Computers and Electronics in Agriculture, 2023. [21] Jeong, J. H., et al., Machine Learning Models for Crop Yield Prediction, Agricultural Systems, 2024. [22] Cisco, IoT and Edge Computing Framework for Smart Farming, Technical White Paper, 2023. [23] Satyanarayanan, M., Edge Computing: Vision and Challenges, IEEE Computer, 2022. [24] Shi, W., et al., Edge Computing: Vision and Challenges, IEEE Internet Computing, 2023. [25] Li, X., et al., Hybrid Edge–Cloud Architecture for Smart Agriculture, Future Generation Computer Systems, 2024. [26] Patil, K., Kulkarni, R., Smart Irrigation System Using IoT and Edge Computing, International Journal of Agricultural Informatics, 2023. [27] Zhang, Y., et al., Edge–Cloud Based Intelligent Irrigation Management, IEEE Access, 2024. [28] Mohanty, S. P., et al., Deep Learning for Smart Agriculture Applications, Computers and Electronics in Agriculture, 2023. [29] Jeong, J. H., et al., Machine Learning-Based Crop Yield Prediction, Agricultural Systems, 2024. [30] Wang, X., et al., Energy-Efficient Edge Computing for IoT Agriculture, IEEE IoT Journal, 2024. [31] Mekki, K., et al., Low Power Wide Area Networks for Smart Farming, ICT Express, 2023. [32] Cisco, IoT and Edge Computing Architecture for Smart Agriculture, White Paper, 2023. [33] Gupta, R., et al., Simulation-Based Performance Evaluation of Smart Agriculture Systems, Journal of Cloud Computing, 2024.

Copyright

Copyright © 2026 Dr. Tanaji. 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.