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Paper Code: AJETAS-2026-0042Open AccessResearch ArticlePeer Reviewed

Deep Learning Architectures for Autonomous Agricultural Monitoring and Analysis

Dr. Sarah Jenkins;Aarav Patel;Dr. Ming-Na Wen
Received: May 15, 2026Accepted: June 20, 2026Published: June 2026
Published inAvrmitra Journal of Engineering, Technology and Applied Sciences
Volume & IssueVol. 4, Issue 2
DOI10.2694/ajetas.2026.042.01

Abstract & Keywords

Autonomous agricultural vehicles require real-time perception to map crop yields, identify weeds, and avoid hazards. This paper evaluates the performance of residual convolutional neural networks (CNNs) trained on multispacial agricultural imagery. We present a custom parameter-pruned architecture suitable for edge deployment on low-power telemetry modules, achieving 94.2% accuracy in weed segmentation.

Index Keywords
Deep LearningConvolutional NetworksSmart FarmingEdge AI

Author Affiliations

Dr. Sarah JenkinsCorresponding Author · Department of Computer Science & Engineering, IIT Bombay, India
Aarav PatelResearch Partner · Department of Computer Science & Engineering, IIT Bombay, India
Dr. Ming-Na WenResearch Partner · Department of Computer Science & Engineering, IIT Bombay, India

References Listing (3)

  1. [1]K. R. Rao and J. Doe, "High-resolution convolutional modeling in thermal imaging systems," IEEE Trans. Image Process., vol. 31, pp. 210–222, Jan. 2024.
  2. [2]W. Liu, R. Chen, and M. Rossi, "Deep residual neural networks for noise-reduction and super-resolution edge rebuilding," Pattern Recognition, vol. 142, pp. 110-125, Oct. 2025.
  3. [3]S. Jenkins, "Cooperative token-heuristics inside warehousing routing grids," Int. J. Rob. Res., vol. 18, no. 4, pp. 450–467, May 2025.