1Dr. Y. Chitti Babu (2026) Ai-Powered Monitoring of Crop Health, Soil Condition, and Pest Risks Using Multispectral/hyperspectral Imaging and Sensor Data

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Identification

• Journal: American Journal of AI Cyber Computing Management
• Year: 2026
• Date: 2026-09-01
• Authors: 1Dr. Y. Chitti Babu,2G. Deepthi,3B. Dayakar Reddy,4D. Balaji Reddy
• DOI: None

Research Groups

Not specified in the provided text.

Short Summary

This paper presents an AI-powered framework that integrates multispectral/hyperspectral imaging with IoT sensor data to monitor crop health, soil condition, and pest risk. The system provides comprehensive field assessments and generates timely recommendations for precision agriculture, demonstrating improved reliability and support for sustainable farming practices.

Objective

• To develop and present an AI-powered framework that integrates multispectral/hyperspectral imaging and IoT sensor data for comprehensive crop health monitoring, soil condition assessment, and pest risk prediction to support precision agriculture.

Study Configuration

• Spatial Scale: Field-level or sub-field level agricultural plots.
• Temporal Scale: Real-time or near real-time continuous monitoring and predictive analysis.

Methodology and Data

• Models used: Machine learning models, predictive analysis algorithms.
• Data sources: Multispectral/hyperspectral imaging, IoT-based sensor data (soil moisture, temperature, humidity, pH, nitrogen, phosphorus, potassium).

Main Results

• The proposed framework provides a comprehensive assessment of field conditions by integrating spectral features from crop images with real-time environmental parameters.
• It enables machine learning models to perform crop health evaluation, nutrient status assessment, and pest risk prediction with improved reliability through a feature fusion approach.
• The system introduces a Crop Health Score to summarize overall field condition, considering plant vigor, soil quality, and environmental stability.
• It generates timely recommendations for irrigation, fertilizer application, and pest management, facilitating preventive interventions.
• The framework demonstrates effectiveness in optimizing agricultural resource utilization, reducing unnecessary chemical inputs, enhancing crop productivity, and promoting sustainable farming practices.

Contributions

• Development of an AI-powered framework that seamlessly integrates heterogeneous data from multispectral/hyperspectral imaging and IoT sensors for comprehensive agricultural monitoring.
• Introduction of a feature fusion approach to enhance the reliability of machine learning models for crop health, nutrient status, and pest risk prediction.
• Creation of a Crop Health Score as a holistic metric for overall field condition in precision agriculture.
• Enabling a shift from reactive to preventive agricultural interventions through real-time monitoring and predictive analysis.
• Demonstrating a practical approach to optimize resource utilization, reduce chemical inputs, and promote sustainable farming.

Funding

Not specified in the provided text.

Citation

@article{1DrYChittiBabu2026AiPowered,
  author = {1Dr. Y. Chitti Babu,2G. Deepthi,3B. Dayakar Reddy,4D. Balaji Reddy},
  title = {Ai-Powered Monitoring of Crop Health, Soil Condition, and Pest Risks Using Multispectral/hyperspectral Imaging and Sensor Data},
  journal = {American Journal of AI Cyber Computing Management},
  year = {2026},
  url = {https://openalex.org/W7217683984}
}