Rayalaseema in southern India is one of the country's most drought-vulnerable regions, experiencing drought in 15 of 18 years between 2000-2018. Over 80% of its 2.7 million farmers are marginal or small landholders practicing rainfed agriculture. Despite pressing agricultural information needs around water management, crop selection, and drought adaptation, these farmers face compounding barriers: 36% rural internet penetration, 60% literacy rates, and information systems designed for English or Hindi speakers. We examine the current landscape of agricultural information retrieval resources for Telugu-speaking farmers and find a critical gap: while agricultural QA datasets exist for Hindi, Tamil, and Bengali, no Telugu agricultural QA benchmark exists, let alone one addressing drought-specific contexts. We document available information sources, identify where they fall short, and propose a methodology for constructing a Telugu Agricultural QA dataset using Kisan Call Center records and regional extension materials. This work charts a path toward inclusive agricultural IR for over 74 million Telugu speakers.

This study presents an efficient no‐reference image quality assessment (NR‐IQA) technique to assess the quality of images affected by noise. The proposed technique is based on two characteristics of the human eye (retina), namely the presence of centre‐surround receptive field and visualisation utilising different spatial frequency channels. In the proposed technique, the authors model centre‐surround receptive field using difference of Gaussians (DoG), whereas to mimic multiple frequencies in the centre‐surround receptive field, they compute multiple DoG images of different values of standard deviations generated for different frequencies. Furthermore, the singular value decomposition‐based features are obtained from the generated DoG images to estimate the image quality. The proposed technique does not require any training, neither based on distorted/original images nor based on subjective human scores, to assess the image quality. The performance of the proposed technique is being analysed on LIVE, TID08, CSIQ and SD‐IVL databases and it shows that the proposed technique outperforms recently proposed NR and no‐training/training‐based IQA techniques. Experimental validation of the proposed technique in the big‐data scenario of 10,000 noisy images also shows encouraging results.

Wireless mesh networks in general, and WiFi mesh networks in particular, offer a cost-effective option to provide broadband connectivity in sparse regions. Effective support for real-time as well as high throughput applications requires a TDMAbased approach. However, multihop TDMA implementations in wireless have been few and far-between, and for good reasons. These present significant issues in terms of time synchronization, TDMA schedule dissemination, multichannel support, routing integration, spatial reuse and so on. And achieving these efficiently, in the face of wireless channel losses presents a formidable challenge. In this work, we present an implementation of LiT MAC, a full-fledged multihop TDMA MAC, on commodity WiFi platforms. We undertake extensive evaluations using microbenchmarks as well as application level performance, using outdoor as well as indoor testbeds. We also present an integration of LiT MAC with various routing metrics, and a routing stability study of recently proposed routing metrics (ROMA, SLIQ). Our results show that we can achieve μs granularity time synchronization across several hops, and TDMA slot size as small as 2 ms. These imply low control overheads. Experiments over several days, on our nine-node outdoor testbed shows that LiT MAC's soft-state-based approach is effective in robust operation even in the presence of significant external interference.