arXiv subjects

Vaishnavi

Publications and source records attributed to Vaishnavi.

3 recordsLinked to original sources

arXiv2026

Input-Output Price Parity and Farm Profitability: A Strategic Perspective for Karnataka

Agricultural pricing policies are crucial for farm profitability and food security in India. This study analysed how input and output prices significantly influence the profitability of cereals in Karnataka, with the strategic support prices playing a crucial role in maintaining the price parity. The average annual TFP growth was 1.041 per cent. Rising input costs, particularly for human labour, led to reduced profitability for Jowar (6.12 per cent) and Ragi (4.89 per cent). The net effect was adverse for Jowar (-1.50 per cent) and Ragi (-0.86 per cent) due to rising input costs outpacing output prices. The study recommended increasing the MSP for Jowar (60 per cent) and Ragi (46.24 per cent) above the existing levels. A strategic price adjusted for changing input costs can stabilise farm incomes and promote sustainable production, enabling efficient pricing policies.

econ.GN↗

arXiv2026

Unlocking AI's Potential in Agriculture: The Critical Role of Data

India generates substantial volumes of public agricultural data, yet artificial intelligence (AI) adoption in farming remains limited and largely confined to pilot initiatives. This paper examines this gap by assessing India's agricultural data infrastructure against the requirements of AI systems deployed at scale. Drawing on a systematic review of major national datasets and digital initiatives including Soil Health Cards, crop insurance, AgriStack, and selected state platforms we identify persistent structural constraints, including temporal misalignment between data collection and agricultural decision cycles, spatial fragmentation arising from the absence of common geocodes linking soil, weather, and yield information, limited machine readability due to reliance on static data formats, and unclear governance frameworks that restrict data access and reuse. These deficiencies impede cross-dataset integration and automated decision support, with disproportionate consequences for smallholders, who constitute 86~\% of India's farmers and lack the capacity to compensate for weak data infrastructure. Drawing on implementation evidence from India and comparative international experiences, the paper identifies recurring features associated with scalable digital agriculture systems, including incentives linked to data provision, service bundling through local institutions, and sensor-enabled risk management.

econ.GN↗

arXiv2024

Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs

The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and integrating modality-specific encoders. However, LMMs are plagued by hallucinations that limit their reliability and adoption. While traditional methods to detect and mitigate these hallucinations often involve costly training or rely heavily on external models, recent approaches utilizing internal model features present a promising alternative. In this paper, we critically assess the limitations of the state-of-the-art training-free technique, the logit lens, in handling generalized visual hallucinations. We introduce ContextualLens, a refined method that leverages contextual token embeddings from middle layers of LMMs. This approach significantly improves hallucination detection and grounding across diverse categories, including actions and OCR, while also excelling in tasks requiring contextual understanding, such as spatial relations and attribute comparison. Our novel grounding technique yields highly precise bounding boxes, facilitating a transition from Zero-Shot Object Segmentation to Grounded Visual Question Answering. Our contributions pave the way for more reliable and interpretable multimodal models.

cs.CL↗