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Plaksha University

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Research library26linked papers
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Selected work

Representative Papers

SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

Aug 11, 2026

This work addresses the challenge of agricultural monitoring under complex temporal, phenological, and climatic dynamics, where existing approaches predominantly rely on optical imagery or multimodal data. The study proposes the first self-supervised learning framework that operates solely on SAR intensity images, introducing an enhanced temporal pretraining task integrated with a tailored masking strategy and curriculum learning to effectively capture phenological features for crop type identification without any optical data. Evaluated on the SICKLE benchmark, the method achieves an IoU of 84.9%, substantially outperforming optical-based baselines by 15.3 percentage points and surpassing current SAR-only approaches by 2.2 percentage points, thereby overcoming a critical bottleneck in all-weather representation learning for agricultural remote sensing.

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CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV

Jul 21, 2026

This study addresses the inefficiency and inaccuracy of manual sugarcane emergence monitoring, which struggles to reliably identify missing-plant areas (“bald patches”). To overcome this, the authors propose an automated pipeline leveraging UAV imagery and YOLOv8-based object detection, enhanced by a novel use of Minimum Spanning Trees (MST) to normalize planting orientation. This approach effectively accommodates diverse field layouts and robustly extracts sugarcane rows. Trained on UAV data from multiple agro-climatic zones, the model converts detection outputs into geospatial point clouds and exports them in Well-Known Text (WKT) format for integration with GIS platforms. The resulting high-resolution emergence maps enable precise replanting guidance, thereby enhancing crop yield, resource-use efficiency, and overall agricultural sustainability.

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OpenLanguageModel: Readable and Composable Small-Language-Model Pretraining for Education and Research

Jul 18, 2026

This work addresses the lack of readable, well-structured, and reusable open-source pretraining frameworks for small language models in education and research by introducing a modular PyTorch-based library. The framework employs composable primitives—such as Block, Residual, Repeat, and Parallel—to ensure alignment between model code and architectural diagrams, enabling seamless transition from pedagogical examples to full-scale pretraining. It integrates streaming data processing, mixed-precision training, callback mechanisms, and single-node multi-GPU support, allowing architecture or component substitution without code modification. Experiments demonstrate that a 348M-parameter model achieves 90.6% weak scaling efficiency across four GPUs, closely matching reference implementations. The project includes 27 preset models, comprehensive documentation, and has received positive early community feedback, effectively bridging teaching, research, and engineering practices.

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Recent publications

Latest Papers

SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

Aug 11, 2026

This work addresses the challenge of agricultural monitoring under complex temporal, phenological, and climatic dynamics, where existing approaches predominantly rely on optical imagery or multimodal data. The study proposes the first self-supervised learning framework that operates solely on SAR intensity images, introducing an enhanced temporal pretraining task integrated with a tailored masking strategy and curriculum learning to effectively capture phenological features for crop type identification without any optical data. Evaluated on the SICKLE benchmark, the method achieves an IoU of 84.9%, substantially outperforming optical-based baselines by 15.3 percentage points and surpassing current SAR-only approaches by 2.2 percentage points, thereby overcoming a critical bottleneck in all-weather representation learning for agricultural remote sensing.

0 citationsRead paper

CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV

Jul 21, 2026

This study addresses the inefficiency and inaccuracy of manual sugarcane emergence monitoring, which struggles to reliably identify missing-plant areas (“bald patches”). To overcome this, the authors propose an automated pipeline leveraging UAV imagery and YOLOv8-based object detection, enhanced by a novel use of Minimum Spanning Trees (MST) to normalize planting orientation. This approach effectively accommodates diverse field layouts and robustly extracts sugarcane rows. Trained on UAV data from multiple agro-climatic zones, the model converts detection outputs into geospatial point clouds and exports them in Well-Known Text (WKT) format for integration with GIS platforms. The resulting high-resolution emergence maps enable precise replanting guidance, thereby enhancing crop yield, resource-use efficiency, and overall agricultural sustainability.

0 citationsRead paper

OpenLanguageModel: Readable and Composable Small-Language-Model Pretraining for Education and Research

Jul 18, 2026

This work addresses the lack of readable, well-structured, and reusable open-source pretraining frameworks for small language models in education and research by introducing a modular PyTorch-based library. The framework employs composable primitives—such as Block, Residual, Repeat, and Parallel—to ensure alignment between model code and architectural diagrams, enabling seamless transition from pedagogical examples to full-scale pretraining. It integrates streaming data processing, mixed-precision training, callback mechanisms, and single-node multi-GPU support, allowing architecture or component substitution without code modification. Experiments demonstrate that a 348M-parameter model achieves 90.6% weak scaling efficiency across four GPUs, closely matching reference implementations. The project includes 27 preset models, comprehensive documentation, and has received positive early community feedback, effectively bridging teaching, research, and engineering practices.

0 citationsRead paper