Integration of IoT and biotechnology for real-time crop monitoring and management in smart agriculture

Document Type : Research Paper

Authors

1 Department of CS & IT, Kalinga University, Raipur, India.

2 Department of CS & IT, Kalinga University, Raipur, India.

10.22103/jab.2025.24001.1609

Abstract

Abstract
Objective
Modern problems including rising food demand, limited resources, and environmental degradation can be effectively addressed through the revolutionary practice of smart agriculture (SA). Meeting global demand while reducing environmental effect is a challenge for traditional farming practices. By enhancing agricultural methods, increasing crop yields, and decreasing resource consumption, the combination of Biotechnology (BT) with SA provides a revolutionary solution.

Material and methods
Smart Agriculture systems' incorporation of data analytics and Deep Neural Networks (DNN) has increased the optimization potential of agriculture even further. In order to improve crop management, decrease waste, and increase overall farm production, farmers can use data-informed decisions made possible by DNN algorithms to get practical insights into crop health, growth trends, and ideal farming practices.

Results
A Real-Time Crop Monitoring and Management (R-CMM) system integrating DNN, Internet of Things (IoT), and Biotechnology (BT) is proposed in this research as an application of Smart Agriculture. By collecting biological signals from the environment using tiny, renewable, and non-invasive sensors, IoBT provides real-time data on plant health, soil conditions, and climate parameters. With this, automated administration of crop systems and continuous monitoring from a distance are both made possible, cutting down on personnel expenses and increasing overall efficiency.

Conclusions
Indoor crop plantation management relies on a number of critical characteristics, including temperature, humidity, soil moisture, and light intensity, all of which the R-CMM system uses to keep checks on. The platform’s use of DNN algorithms allows for more effective and accurate farming by predicting when crops may experience stress, optimizing the allocation of resources, and detecting early indications of disease or pest infestations.

Keywords

References

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Agricultural Biotechnology Journal
Volume 16, Issue 4
January 2025
Pages 307-320

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