SmartSoil: A Humanoid-Inspired Drone Framework Using Thermal Imaging and AI for Real-Time Soil Moisture Estimation

B. V. Shruti; R. S. Shoma; C. M. Vijaya Madhavi; H. R. Sridevi; N. R. Pallavi; C. Lohith; A. Gnanasundari; M. Vaishnavi; P. Raghu

doi:10.48084/etasr.17382

Authors

B. V. Shruti CSE (AI & ML), Global Academy of Technology, Bengaluru, Karnataka, India
R. S. Shoma Department of ISE, Cambridge Institute of Technology, K R Puram, Bengaluru, Karnataka, India
C. M. Vijaya Madhavi Department of Electronics and Telecommunication Engineering, MSRIT, Bengaluru, Karnataka, India
H. R. Sridevi Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India
N. R. Pallavi Department of CSE, BGSIT, Adichunchanagiri University, BG Nagara, Mandya, Karnataka, India
C. Lohith Department of CSE (IoT & CSBT), East Point College of Engineering and Technology, Bengaluru, Karnataka, India
A. Gnanasundari Dr. A P J Abdul Kalam School of Engineering, Garden City University, Bengaluru, Karnataka, India
M. Vaishnavi School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India
P. Raghu Department of ISE, Cambridge Institute of Technology, K R Puram, Bengaluru, Karnataka, India

Volume: 16 | Issue: 2 | Pages: 33820-33826 | April 2026 | https://doi.org/10.48084/etasr.17382

Received: 6 January 2026 | Revised: 26 January 2026 and 13 February 2026 | Accepted: 14 February 2026 | Online: 24 February 2026

Corresponding author: C. Lohith

Abstract

The growing need for environmentally friendly farming and improved irrigation necessitates smart systems that can see, think, and act autonomously. Ground sensors, satellite images, and thermal Unmanned Aerial Vehicle (UAV) mapping are traditional approaches to measuring soil moisture, but they suffer from low spatial resolution, slow data processing, and limited adaptability to changing field conditions. To overcome these limitations, this research introduces SmartSoil, a humanoid-inspired autonomous framework that amalgamates multi-sensor drone-based thermal imaging, Artificial Intelligence (AI)-driven soil analytics, and edge-computing cognition for real-time soil moisture estimation. The SmartSoil system mimics human cognitive processes through three stages: perception, cognition, and action. The UAV platform serves as the sensory organ, equipped with thermal, RGB, and Ground-Penetrating Radar (GPR) sensors to collect data from both surface and subsurface layers. The cognitive core uses Random Forest Regression (RFR) and Artificial Neural Networks (ANN) to interpret thermal, spectral, and textural features extracted from field imagery. The edge-AI module, on the other hand, acts like a human brain by automatically calibrating emissivity, correcting atmospheric distortions, and generating soil moisture predictions with rapid adaptive capability. The action layer visualizes the results as geo-referenced moisture maps, supporting real-time irrigation decision-making. AI-only UAV systems refer to drone-based soil moisture estimation systems that rely solely on Machine Learning (ML) models applied to single-sensor data without multi-sensor fusion or adaptive edge-based cognition. The experiments were conducted in three semi-arid agricultural zones (clay loam, sandy loam, and silty loam). The humanoid SmartSoil system achieved a Root Mean Square Error (RMSE) of 3.1%, a Mean Absolute Error (MAE) of 2.3%, and a coefficient of determination (R²) of 0.94. This performance exceeded that of traditional AI-only UAVs (RMSE: 5.2%) and thermal-only UAVs (RMSE: 6.4%). SmartSoil also demonstrated a 27% reduction in latency and a 95% increase in processing efficiency, indicating enhanced cognitive responsiveness and improved real-time adaptability to dynamic field conditions.

Keywords:

Artificial Neural Networks (ANN), cognitive automation, edge AI computing, humanoid intelligence, multi-sensor data fusion, precision agriculture, Random Forest Regression (RFR), SmartSoil framework, soil moisture estimation, UAV thermal imaging

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References

R. Díaz-Delgado et al., "Mapping Soil Moisture Using Drones: Challenges and Opportunities," Engineering Proceedings, vol. 94, no. 1, Aug. 2025, Art. no. 18.

Y. Inoue, "Satellite- and drone-based remote sensing of crops and soils for smart farming – a review," Soil Science and Plant Nutrition, vol. 66, no. 6, pp. 798–810, Nov. 2020.

J. Paul, "Assessment of Soil Moisture Variability using Satellite Imagery and Drone-based Thermal Mapping." ResearchGate, July 18, 2025.

H. S. Ndlovu, J. Odindi, M. Sibanda, and O. Mutanga, "A systematic review on the application of UAV-based thermal remote sensing for assessing and monitoring crop water status in crop farming systems," International Journal of Remote Sensing, vol. 45, no. 15, pp. 4923–4960, Aug. 2024.

Q. Chen and Z. Zhang, "Feasibility Study on Use of Drone-Based Infrared Thermography for Soil Moisture Detection in Highway Embankment and Dam Inspections," Journal of Infrastructure Systems, vol. 31, no. 1, Mar. 2025, Art. no. 04024033.

D. G. H. Agrawal, N. Paunikar, N. More, H. Dekate, and S. Nikam, "Smart Drone Technology for Thermal Imaging," International Journal of Creative Research Thoughts, vol. 13, no. 4, pp. b943–b951, Apr. 2025.

M. Vahidi, S. Shafian, and W. H. Frame, "Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning," Sensors, vol. 25, no. 3, Jan. 2025, Art. no. 782.

L. Bertalan et al., "UAV-based multispectral and thermal cameras to predict soil water content – A machine learning approach," Computers and Electronics in Agriculture, vol. 200, Sept. 2022, Art. no. 107262.

M. Vahidi, S. Shafian, and W. H. Frame, "Multi-Modal sensing for soil moisture mapping: Integrating drone-based ground penetrating radar and RGB-thermal imaging with deep learning," Computers and Electronics in Agriculture, vol. 236, Sept. 2025, Art. no. 110423.

P. Gayathri, D. Muthumanickam, S. Pazhanivelan, R. Kumaraperumal, P. Kannan, and P. C. Prabu, "Emerging trends in AI-based soil health assessment: A review," Plant Science Today, vol. 12, no. 3, pp. 1–14, July 2025.

N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019.

R. Al-Najadi, Y. Al-Mulla, I. Al-Abri, and A. M. Al-Sadi, "Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions," Scientific Reports, vol. 15, no. 1, Mar. 2025, Art. no. 9042.

Y. Han et al., "Calibration and Image Processing of Aerial Thermal Image for UAV Application in Crop Water Stress Estimation," Journal of Sensors, vol. 2021, no. 1, Jan. 2021, Art. no. 5537795.

D. Y. Lim, I. J. Jin, and I. C. Bang, "Heat-vision based drone surveillance augmented by deep learning for critical industrial monitoring," Scientific Reports, vol. 13, no. 1, Dec. 2023, Art. no. 22291.

K. A. Henn and A. Peduzzi, "Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments," Remote Sensing, vol. 16, no. 5, Mar. 2024, Art. no. 930.

T. X. B. Nguyen, K. Rosser, and J. Chahl, "A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation," Journal of Imaging, vol. 7, no. 10, Oct. 2021, Art. no. 217.

A. Santangeli, Y. Chen, E. Kluen, R. Chirumamilla, J. Tiainen, and J. Loehr, "Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land," Scientific Reports, vol. 10, no. 1, July 2020, Art. no. 10993.