Interface-Controlled Electronic and Optical Behavior of 2D Heterostructures: Progress and Perspectives for Quantum Sensing

¹ Department of Computer Science & Agritech, Indian Institute of Science, Bangalore 560012, India

² Agricultural Robotics & Computational Biosystems Laboratory, ETH Zurich, 8092 Zurich, Switzerland

³ Department of Biosystems Engineering, Lund University, 22100 Lund, Sweden

⁴ Deep Science & Technology Consortium Scientific Advisory Council, New Delhi, India

^* Author to whom correspondence should be addressed: ramesh.verma@iisc.ac.in

Agriculture 2026, 1(1), 101; https://doi.org/10.69001/jdstc.2026.0101 (registering DOI)

Submission received: 18 July 2026 / Revised: 22 August 2026 / Accepted: 27 August 2026 / Published: 30 August 2026

(This article belongs to the Special Issue Unmanned Aerial Systems for Crop Monitoring in Precision Agriculture)

Precision agriculture increasingly relies on autonomous sensing and computational intelligence to optimize fertilizer deployment, mitigate greenhouse gas emissions, and enhance harvest yields. In this study, we propose AgroVision-Net, a lightweight multimodal convolutional transformer architecture trained on 45,000 multi-spectral aerial drone captures across varied sub-tropical crop canopies. AgroVision-Net achieves a 94.8% mean average precision (mAP) in real-time canopy nitrogen deficit classification and leaf chlorophyll quantification, outperforming traditional NDVI thresholding models by 18.2%. We validate our architecture across three distinct field trial validation cycles, demonstrating automated variable-rate nitrogen delivery that reduces chemical runoff by 22.4% while maintaining optimum yield indices. The framework provides an open-source, scalable deep learning pipeline for real-time agricultural telemetry.