Krishi Prahari: An IoT and ML-Based Framework for Smart Agricultural Threat Detection and Resource Optimization

Abstract

Agriculture remains the backbone of India’s economy, but it is increasingly vulnerable to a variety of critical threats such as climate variability, crop fires, wild animal intrusions, and poor resource utilization. These challenges not only reduce crop yield but also lead to long-term damage to soil fertility, farmer income instability, and increased water consumption. In this context, the paper introduces KrishiPrahari, an intelligent, IoT and Machine Learning-powered agricultural management system developed to detect threats at an early stage and optimize decision-making processes in real time. Using an array of linked sensors, which input data into machine learning algorithms for predictive analysis, the system is meant to constantly monitor environmental conditions. This provides for early alerts for fire threats, tracking of animal movement in agricultural areas, and irrigation optimization dependant on soil conditions. By providing a proactive, automated, low-cost strategy adaptable across multiple climatic and geographical zones, the recommended solution fills in the deficiencies in existing reactive solutions. Water consumption efficiency has increased considerably, crop protection from animals and fire has been boosted, and local farmers\' simplicity of use has been proven by rigorous field testing. By strengthening farm-level situational awareness and resource management capacities, KrishiPrahari consequently provides a crucial step toward obtaining climate-resilient and precision agriculture in India [1].

Introduction

India’s agricultural environment is diverse and highly sensitive to natural and human-made challenges, including frequent hazards like agricultural fires, wildlife crop damage, and water management issues. Traditional farming methods are often reactive and inefficient, leading to soil degradation, economic loss, and resource waste. Although digital infrastructure has improved, rural farmers face barriers like high costs, lack of awareness, and training gaps in adopting technology.

Recent advances in IoT (Internet of Things) and Machine Learning (ML) offer promising solutions. The KrishiPrahari system integrates real-time sensor data, automation, and predictive analytics to address fire detection, wildlife intrusion, and irrigation management. It shifts farming from manual, reactive practices to data-driven, automated, and predictive operations, improving productivity, sustainability, and resilience.

The system uses multi-layer architecture with sensors for environmental monitoring, wireless communication, ML-powered data processing, automated responses (e.g., irrigation control, alerts), and user-friendly interfaces accessible to rural farmers. Solar-powered hardware ensures off-grid operation. Experimental results show high accuracy in fire and wildlife detection, significant water savings (~32%), faster fire response (<5 seconds vs. 30+ minutes), increased crop yields, and strong user acceptance.

Conclusion

Agricultural sustainability in India faces serious difficulties due to weather unpredictability, recurring crop fires, wild animal invasions, and inadequate water management. KrishiPrahari was created and constructed as an integrated, data-driven response to these difficulties delivering a single platform for threat detection, predictive decision-making, and automated action leveraging low-cost hardware and intelligent algorithms. The system combines the capabilities of Internet of Things (IoT) sensors and Machine Learning (ML) models to deliver real-time environmental monitoring, early warning messages, and precision irrigation. Through field deployment and testing, KrishiPrahari displayed outstanding sensor accuracy (fire detection: 96.4%, wildlife detection: 94.1%) and consistent ML performance (F1-score: 0.93 for animal classification, irrigation MSE: 4.3%). These results demonstrate its skills to perform under real-world agricultural circumstances. KrishiPrahari modular architecture permits for scalability, while its solar-powered operation and wireless connection make it suited for off-grid and rural agricultural environments. Its user-friendly interface offered through internet and mobile platforms minimizes the technical learning curve and allows quick farmer adoption. Moreover, comparative research indicated a 32% reduction in seasonal water usage, a 22% rise in average production per acre, and a significant reduction in reaction time to serious threats from almost 30 minutes to less than 5 seconds. These adjustments instantly lead to improved agricultural productivity, lower input costs, and enhanced food security. In conclusion, KrishiPrahari is a realistic, cheap, and substantial smart agriculture solution. It bridges the technological barrier in rural farming and empowers producers with automation and data-driven insights. With future development such as drone integration, multi-language support, and AI-based pest identification it holds the potential to become a cornerstone in India’s agri-tech transformation and a repeatable model for sustainable farming internationally.

References

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Copyright

Copyright © 2025 Shubhanshu Singh, Amit Singh, Aditi Singh, Kriti , Ankur Kumar, Anil Kumar Chaudhary. 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.