Background Land acquisition is one of the most critical and time-sensitive phases of infrastructure development. Delays in acquiring land significantly impact the execution of national and state-level projects. The causes of land acquisition delays are multifaceted, including prolonged administrative approvals, legal disputes, delayed compensation disbursement, incomplete documentation, pending notifications, land ownership conflicts, rehabilitation and resettlement challenges, and inter-departmental coordination issues. Description of the Study Develop an AI-powered Predictive Analytics System capable of identifying land acquisition projects that are at risk of delay by analyzing historical and real-time project data. The proposed solution should utilize machine learning algorithms to study patterns from completed and ongoing land acquisition cases, considering parameters such as project type, land area, number of affected families, compensation status, approval timelines, legal disputes, possession status, rehabilitation progress, stakeholder responsiveness, and historical performance. The system should generate a risk score for each project and predict the probability of delays at different stages of the land acquisition lifecycle. It should also identify the key contributing factors responsible for the predicted delay and provide actionable recommendations for mitigating those risks. Interactive dashboards should enable policymakers and administrators to monitor high-risk projects, visualize delay trends across districts and states, and prioritize interventions based on predictive insights. The solution should support continuous learning by updating prediction models as new project data becomes available, thereby improving prediction accuracy over time. Add 'Scope of Study' Table here Problems There is no intelligent mechanism capable of identifying projects that are likely to experience delays before they occur. With the availability of large volumes of historical land acquisition data, project timelines, administrative records, and geospatial information, Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to predict potential delays, identify risk factors, and enable proactive interventions. Such a predictive system would significantly improve planning, monitoring, resource allocation, and decision-making for infrastructure projects across the country. Expected Solution The proposed solution should be an AI-enabled decision support platform capable of predicting potential land acquisition delays before they adversely impact project implementation. The solution should provide 7. AI/ML-based predictive models for forecasting project delays. 8. Automated identification of projects with high probability of delay. 9. Project-wise risk scoring and prioritization based on multiple parameters. 10. Identification of key delay drivers such as pending approvals, compensation delays, legal disputes, incomplete documentation, rehabilitation status, and administrative bottlenecks. 11. Explainable AI techniques to ensure transparency in prediction results. Interactive dashboards displaying Delay probability, Risk categorization, District-wise and State-wise delay trends, Timeline analysis, Performance indicators, Comparative analytics 7. GIS-enabled visualization of high-risk projects on digital maps. 8. Automated alerts and notifications for project managers and administrators. 9. Predictive recommendations suggesting corrective actions to minimize delays. 10. Continuous model learning using newly generated project data for improved prediction accuracy. 11. APIs for integration with existing land acquisition management systems and government databases. 12. Secure, role-based access for various stakeholders with comprehensive audit trails. The proposed solution should enable proactive governance by shifting project monitoring from reactive reporting to predictive decision-making, thereby reducing project delays, optimizing public expenditure, and accelerating infrastructure development. Add 'Suggested components-wise technology' table here

Background Packaged commodities are widely sold through retail stores, supermarkets and e-commerce platforms across India. Under the Legal Metrology Act, 2009 and the Legal Metrology(Packaged Commodities) Rules, 2011, every packaged commodity is required to bear mandatory declarations such as name and address of manufacturer/packer/importer, net quantity, Maximum Retail Price (MRP), month and year of manufacture/packing/import,consumer care details and other prescribed declarations in a specified format and manner.These declarations are important for ensuring transparency, fair trade practices and consumer protection. However, due to the large volume and variety of packaged products available in the market, manual inspection and compliance checking by enforcement agencies becomes time-consuming and resource intensive. Non-compliance such as missing declarations, incorrect font sizes, improper MRP declarations and other such practices are frequently observed.There is scope to develop a compliance checking system capable of scanning product labels,package images and product listings to identify violations under the Legal Metrology(Packaged Commodities) Rules, 2011. Accordingly, a software system capable of automatically detecting, extracting and validating mandatory declarations and identifying noncompliances in packaged commodities through image and label analysis can be developed. Description Develop a software application capable of scanning packaged commodity labels, product images and product information to automatically assess compliance with the Legal Metrology(Packaged Commodities) Rules, 2011. The system should be capable of Scanning and analyzing images of packaged commodities. Detecting mandatory declarations prescribed under Legal Metrology rules. Checking correctness, completeness and placement of declarations. Identifying missing or non-compliant declarations. Checking readability and font size requirements. Generating compliance reports and violation summaries. Maintaining a repository of scanned products and compliance history. Providing dashboards for enforcement officials. Expected Solution The proposed solution should include: User-friendly web and/or mobile-based software application. Automated extraction and validation of mandatory declarations. Rule-based compliance checking for Legal Metrology (Packaged Commodities) Rules, 2011. Generation of digital compliance reports in PDF and editable formats. Dashboard for monitoring inspections, violations and product compliance details. Search and retrieval facility for previously scanned products and reports. Technical documentation describing software architecture and deployment framework. Key Functional Requirements Image upload and product scanning functionality. Extraction of declarations from labels and packaging and detection of mandatory declarations Font size and readability analysis. Detection of missing, misleading or non-standard declarations. Generation of compliance/non-compliance reports. Attachment of photographs and supporting evidence. Repository of scanned products and inspection history. Role-based user access and secure authentication. Dashboard for monitoring compliance status and enforcement activities. Export of reports to PDF and editable formats.

Background The ambient atmospheric condition affects the temperature inside the shelter and makes thermal management necessary for maintenance of temperature in the comfortable range. The existing shelters for any region are generally not designed as per the requirements of a particular region and hence not energy efficient thus demands external thermal comfort maintenance system. Area specific designed shelters looks smart and one time solution for thermal management as per the atmospheric condition of the region. This model development project work is specifically conceptualised keeping in mind the tough climatic condition of High Altitude cold Region like Ladakh and can be used for studying the design requirements of other climatic region shelters as well. Description The Ladakh region is blessed with high solar energy irradiance (1900-2100 kwh/m2/year) along with long average sunshine duration of 7. 9 hours with 300 plus average annual cloud free days. The temperatures inside the shelters found suitable during day hours even during the winter period due to trapping of thermal energy from solar radiation, but approach nearly the ambient atmospheric temperature after sunset. High thermal losses through the material of the shelter and openings contributed towards such low temperature inside shelters. A detailed thermal analysis of the shelter including size, shape orientation etc. along with, study related to application of suitable materials and application of thermal mass storage material, composite multi-material etc. and effect of openings on outcome looks to be a potential solution for development of self-sufficient passive shelter for the region in terms of temperature maintenance. A general model development in ANSYS software to thermally simulate the shelter for study of heat losses and capture of real time atmospheric ambient climatic condition data will prove to be helpful. The project is conceptualised for the development of general model in ANSYS software. The model should be user friendly and works on user defined values (real time data, material properties etc.)to simulate the cases along with comparative analysis with different materials under same ambient condition to predict the most efficient combination of materials, shape, and size etc.in terms of temperature maintenance. The primary objective of the work/project is to minimize the energy utilisation for thermal comfort maintenance in particular and minimization of fossil fuel application in general by designing of area specific self-sustained standalone passive shelter at defined atmospheric climatic condition. Expected Solution Development of Software based model for predicating the suitable shelter design including suitable material, size, shape etc. with the objective of thermal comfort maintenance in passive shelter installed in different atmospheric conditions. This work involves simple feeding of collected data and material properties in developed model and outcome shows in terms of most efficient design with materials for thermal comfort maintenance in a particular region The Developed Model should be capable of giving/solving the following tasks: 1. Prediction of shelter inside temperature based on the user defined inputs. 2. Prediction of thermal energy generated from solar radiation. 3. Heat flow details as per the temperature difference between ambient and shelter temperature for a defined time period.

Background Animal nutrition directly affects milk production, animal health, reproductive performance, and dairy profitability. Dairy farmers often face challenges due to poor-quality cattle feed,adulterated feed ingredients, fungal contamination, toxin presence, and low-quality silage.Conventional feed testing laboratories are expensive and inaccessible for many rural farmers.There is a need for rapid, portable, affordable, and digitally enabled feed quality assessment systems.Emerging technologies such as Al, loT, spectroscopy, computer vision, and biosensors can help create real-time feed testing and advisory systems for dairy farmers. Description Participants are required to develop a rapid digital testing solution capable of: Assessing nutritional quality of cattle feed and silage; Detecting adulteration and contamination; Providing instant farmer advisories and feed recommendations; Monitoring feed storage and silage conditions. The solution may include Portable testing devices; Smartphone-enabled feed analysis; Al-powered nutritional prediction; Cloud dashboards; QR-based authenticity systems. The system may detect Crude protein Moisture Fiber Energy value Mineral deficiencies Urea adulteration Sand/silica contamination Aflatoxins and mycotoxins Fungal contamination Silage monitoring may include pH Fermentation quality Moisture Spoilage indicators Mould growth Expected Solution The expected solution should Provide testing results within minutes; Be low-cost and portable; Support multilingual farmer interfaces; Work offline in rural areas; Generate nutritional and storage advisories; Enable cloud-based monitoring and traceability. Expected technologies AI/ML loT sensors NIR spectroscopy Mobile applications Computer vision Cloud analytics Predictive advisory systems Insert Table Here*

Problem Description Livestock owners, field veterinarians,para-veterinary workers and government departments often lack a unified, realtime mechanism to identify emerging animal-health risks at the village, block and district levels. Disease symptoms may be reported late, diagnostic facilities may be distant, vaccination and treatment histories may be incomplete, and information from farms, veterinary dispensaries, laboratories, vaccination drives and surveillance programmes may remain fragmented. These gaps can delay containment, increase livestock mortality and productivity loss, raise the risk of zoonotic transmission, and affect farmers’ incomes. The challenge is to create a practical system that enables early warning, rapid reporting, risk assessment, preventive action, referral and coordinated response, including in low-connectivity areas. Expected Solution / Outcome A scalable animal-health surveillance and decision-support solution that can: capture symptom and mortality reports from farmers and field workers; use rulebased or AI-assisted triage to flag suspected outbreaks; integrate geospatial risk mapping, weather and historical disease trends; maintain animal-level or herd-level health,vaccination and treatment records; issue multilingual advisories and alerts; support sample collection, laboratory referral and case escalation; provide dashboards for veterinary officials; and operate through mobile, web, IVR or offline-enabled channels. Expected outcomes include reduced reporting time, earlier outbreak identification, improved vaccination coverage, faster treatment and containment, lower mortality and productivity loss, and stronger evidence-based planning.

Problem Description Farmers often recognise crop diseases or pest infestations only after visible damage has spread. Extension staff may cover large areas, while laboratory diagnosis and expert advice may not be immediately available. Weather, crop stage, variety, soil condition and local pest history influence risk, but these inputs are rarely combined into actionable farm-level alerts. Incorrect diagnosis may lead to delayed treatment, excessive or inappropriate pesticide use, increased cultivation cost,residue concerns and yield loss. The challenge is to provide timely, reliable and locally relevant detection,forecasting and management support. Expected Solution / Outcome A farmer- and extension-worker-friendly crop-health system that supports image based symptom identification, pest-trap or sensor inputs, weather-based risk forecasting, geospatial hotspot mapping,expert validation and multilingual advisories. The system should recommend integrated pest and disease management actions, safe input usage,referral to extension or laboratories, and follow-up monitoring. It should learn from field confirmations and provide dashboards for agriculture officials.Expected outcomes include earlier detection, reduced crop loss, more targeted pesticide use, faster extension response, improved surveillance coverage and better planning of preventive interventions.

Problem Description Many farmers, especially smallholders and producer groups, have limited visibility of current and expected prices across nearby markets, processors,institutional buyers and digital trading channels. Information on quality specifications, demand, logistics,storage, payment reliability and buyer credentials may be fragmented. Farmers may sell immediately after harvest because of liquidity or storage constraints and may have weak bargaining power. Buyers, meanwhile,may struggle to aggregate consistent volumes and verify quality. The challenge is to improve transparent price discovery and create reliable, efficient linkages from farm gate to suitable buyers. Expected Solution / Outcome A market-intelligence and transaction enablement solution that aggregates mandi prices, buyer demand, quality requirements, arrival volumes, transport and storage options; provides localised price trends and sale-window recommendations; matches farmers/FPOs with verified buyers; enables lot creation, quality grading,digital offers, logistics coordination and payment tracking; and supports dispute or grievance processes. Expected outcomes include improved farmer price realisation, reduced information asymmetry, lower transaction cost,stronger FPO aggregation, reduced post harvest loss, more reliable buyer sourcing and transparent transaction records.

Developing solutions, keeping in mind the need to enhance the primary sector of India Agriculture and to manage and process our agriculture produce.