VERDANT TERRA: An Acoustic and AI-Driven Framework for Real-Time Plant Stress Management in Precision Agriculture

Abstract

Precision agriculture demands innovative solutions for early and accurate plant stress detection to optimize growth and yield. Existing methods often lack real-time capabilities and proactive environmental control. This research presents VERDANT TERRA, an automated plant stress management system utilizing ultrasonic sensors and machine learning (ML). VERDANT TERRA captures stress-induced acoustic emissions from plants, imperceptible to human hearing. These signals are then analyzed by a custom-trained ML algorithm, enabling real-time stress identification and classification. The system integrates with environmental control mechanisms, automatically adjusting parameters like irrigation and lighting based on the detected stress levels. Experimental results demonstrate VERDANT TERRA\'s efficacy in identifying and responding to various stress factors, including dehydration and nutrient deficiencies, ultimately enhancing plant growth and productivity. VERDANT TERRA offers a cost-effective and scalable solution for precision agriculture, paving the way for sustainable and optimized crop management

Introduction

VERDANT TERRA is an AI-based agricultural system designed to detect and respond to plant stress in real-time using ultrasonic sound emissions and machine learning. As agriculture faces increasing pressure from climate change, resource scarcity, and urbanization, this system offers a highly automated solution ideal for Controlled Environment Agriculture (CEA) and urban farming.

Core Concept:

Plants under stress (due to drought, disease, or damage) emit ultrasonic acoustic signals. VERDANT TERRA uses sensors to detect these emissions and applies AI/ML models to interpret, predict, and respond by adjusting environmental factors such as light, temperature, and irrigation.

System Components: 5 Integrated Frameworks

1. Plant Stress Detection Framework (PSDF):

   • Uses ultrasonic microphones (20–100 kHz) to detect stress signals like xylem cavitation.

   • Continuously monitors for early signs of stress such as drought or nutrient deficiency.

2. Data Collection and Analysis Framework (DCAF):

   • Converts acoustic signals into stress insights using:

     • SVM: Detects if the plant is stressed.

     • Random Forest: Classifies stress type.

     • LSTM networks: Tracks stress development over time.

   • Provides adaptive and intelligent monitoring.

3. Environmental Control Framework (ECF):

   • Responds to stress by adjusting environmental variables:

     • UV light regulation, PID-based temperature control, and automated irrigation.

   • Maintains optimal growth conditions in a closed feedback loop.

4. Predictive Analysis Framework (PAF):

   • Forecasts plant stress using LSTM-based models and simulation scenarios.

   • Proactively prevents stress by adjusting conditions before stress fully develops.

5. User Interface and Reporting Framework (UIRF):

   • Offers a dashboard showing real-time plant data and system status.

   • Sends alerts via email/SMS and generates regular reports.

   • Allows remote access and integrates with farm management tools.

Impact:

• Provides non-invasive, early stress detection.

• Enables real-time, intelligent environmental control.

• Supports urban and precision agriculture through automation and AI.

• Enhances crop health, resilience, and food security—especially important as urban populations grow.

Conclusion

VERDANT TERRA represents a transformative step in precision agriculture by uniting plant bioacoustics, artificial intelligence, and automated environmental control into a cohesive, real-time stress management system. Its modular architecture not only enables early detection and accurate classification of plant stress but also delivers timely, data-driven interventions that enhance crop health and yield. With its predictive intelligence and user-centric interface, VERDANT TERRA offers a scalable, future-ready solution for sustainable farming across diverse agricultural environments.

References

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Copyright

Copyright © 2025 Achitya Kushwaha, Abhishek Singh, Suchita Debnath. 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.