i-manager Publications

An IoT-Enabled Machine Learning System for Efficient Disease Detection and Crop Management in Green Gram Cultivation

Pushpendra Singh*, Sakshi Mishra**, Samistha Patle***, Shrijal Verma****

*-**** Department of Electronics and Telecommunication Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Periodicity:July - December'2025

Abstract

This work presents an advanced plant care system for green gram cultivation using sensors, IoT, and machine learning to monitor real-time environmental factors such as temperature, humidity, and soil moisture. The system's key feature is its ability to predict diseases like powdery mildew and anthracnose by analyzing trends in these parameters, enabling early intervention and preventing yield loss. The machine learning model achieved an overall accuracy of 94%, with high performance across multiple diseases: for Disease 1 (D1), it had a precision of 0.70 and recall of 0.75, while Disease 2 (D2) showed a precision of 1.00 and recall of 0.80. Disease 3 (D3) had perfect precision but lower recall at 0.50, and both Disease 4 (D4) and healthy plants were identified with 100% precision and recall. The system also offers a graphical interface through IoT for remote monitoring, enabling farmers to track key parameters in real time. In critical conditions, it generates alerts, allowing manual control of irrigation to ensure optimal plant health and growth. The combination of IoT and machine learning provides a comprehensive solution to enhance crop care and productivity in green gram farming.

Keywords

IoT-Based Agriculture, Green Gram Disease Detection, Machine Learning, Multilayer Perceptron (MLP), Real- Time Crop Monitoring, Smart Irrigation System.

How to Cite this Article?

Singh, P., Mishra, S., Patle, S., and Verma, S. (2025). An IoT-Enabled Machine Learning System for Efficient Disease Detection and Crop Management in Green Gram Cultivation. i-manager’s Journal on IoT and Smart Automation, 3(2), 31-43.

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An IoT-Enabled Machine Learning System for Efficient Disease Detection and Crop Management in Green Gram Cultivation

Abstract

Keywords

How to Cite this Article?

References

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