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Set A - Question 2

Question Details​

A research scholar has developed a drone-based precision agriculture system for monitoring crop health in semi-arid regions. The proposed system integrates multispectral imaging sensors, GPS navigation, edge computing, and machine learning-based disease detection.

  • A. Identify suitable national funding agencies for the proposed research and justify the suitability of any two agencies based on their research scope and funding objectives. (5 Marks)
  • B. Conduct an audience analysis for the proposed research and explain how the presentation should be tailored for researchers, agricultural extension officers, policy makers, and farming community representatives. (5 Marks)
  • C. Sharma, A., & Nair, V. (2023). Machine learning approaches for crop disease detection using UAV imagery. Journal of Agricultural Informatics, 14(3), 78-92. Convert the given journal reference into IEEE, Chicago, APA, MLA, and Harvard referencing styles. (5 Marks)
  • D. Identify the key criteria for selecting a suitable journal for publication and justify your choice. (5 Marks)

Model Answer​

A. Identify suitable national funding agencies for the proposed research and justify the suitability of any two agencies based on their research scope and funding objectives. (5 Marks)​

1. National Funding Agencies Identified (1 Mark)​

  • DST - Department of Science and Technology (NM-ICPS scheme)
  • ICAR - Indian Council of Agricultural Research (NASF scheme)
  • SERB / ANRF - Science and Engineering Research Board / Anusandhan National Research Foundation
  • MeitY - Ministry of Electronics and Information Technology

2. Justification of Two Selected Agencies (4 Marks)​

  1. Department of Science and Technology (DST) - NM-ICPS (2 Marks):
    • Scope: Supports interdisciplinary Cyber-Physical Systems, IoT sensing, UAV hardware, and embedded edge AI.
    • Funding Objectives: Promotes deep-tech prototype innovation (TRL 3-7) to build climate-resilient technological solutions for national challenges.
  2. Indian Council of Agricultural Research (ICAR) - NASF (2 Marks):
    • Scope: Apex body for strategic agricultural development, dryland farming, and pest/disease management.
    • Funding Objectives: Funds translational research bridging lab-to-land, providing field trial validation through Krishi Vigyan Kendras (KVKs) and extension networks.

B. Conduct an audience analysis for the proposed research and explain how the presentation should be tailored for researchers, agricultural extension officers, policy makers, and farming community representatives. (5 Marks)​

Stakeholder (1.25 M each)Core InterestTailored Content & Delivery Strategy
1. Academic ResearchersAlgorithmic novelty, sensor calibration, accuracy.Deep technical presentation on edge ML architectures, multispectral vegetation indices (NDVI/NDRE), confusion matrix, and mAP scores.
2. Extension OfficersDiagnostic reliability, field advisory workflows.Operational step-by-step procedures, false-alarm interpretation, and disease advisory dissemination protocols.
3. Policy MakersScalability, food security, budget/ROI.Executive summary, cost-benefit analysis, district-wide impact metrics, and alignment with digital agriculture policies (e.g., PMFBY).
4. Farming RepresentativesInput cost savings, ease of operation.Non-technical visual storytelling, vernacular language, simple color-coded disease maps, and practical savings (Rs. saved per acre).

C. Reference Style Conversions (5 Marks)​

Source: Sharma, A., & Nair, V. (2023). Machine learning approaches for crop disease detection using UAV imagery. Journal of Agricultural Informatics, 14(3), 78-92.

  • 1. IEEE Style (1 Mark):
    [1] A. Sharma and V. Nair, "Machine learning approaches for crop disease detection using UAV imagery," J. Agric. Inform., vol. 14, no. 3, pp. 78-92, 2023.
  • 2. Chicago Style - Author-Date (1 Mark):
    Sharma, A., and V. Nair. 2023. "Machine Learning Approaches for Crop Disease Detection Using UAV Imagery." Journal of Agricultural Informatics 14 (3): 78-92.
  • 3. APA Style - 7th Edition (1 Mark):
    Sharma, A., & Nair, V. (2023). Machine learning approaches for crop disease detection using UAV imagery. Journal of Agricultural Informatics, 14(3), 78-92.
  • 4. MLA Style - 9th Edition (1 Mark):
    Sharma, A., and V. Nair. "Machine Learning Approaches for Crop Disease Detection Using UAV Imagery." Journal of Agricultural Informatics, vol. 14, no. 3, 2023, pp. 78-92.
  • 5. Harvard Style (1 Mark):
    Sharma, A. and Nair, V., 2023. Machine learning approaches for crop disease detection using UAV imagery. Journal of Agricultural Informatics, 14(3), pp.78-92.

D. Identify the key criteria for selecting a suitable journal for publication and justify your choice. (5 Marks)​

1. Key Journal Selection Criteria (3 Marks)​

  1. Aims & Scope: Close thematic fit with UAV remote sensing, edge computing, and agriculture.
  2. Indexing & Visibility: Inclusion in major databases: Web of Science (SCI/SCIE), Scopus, and UGC-CARE.
  3. Bibliometric Metrics: High Journal Impact Factor (JIF) and Q1/Q2 Quartile ranking in Agronomy/AI.
  4. Peer-Review Integrity: Established publisher, rigorous double-blind review, adherence to COPE ethics.
  5. Turnaround Time & Fees: Realistic review turnaround (2-4 months) and transparent Article Processing Charges (APC).

2. Selected Journal & Justification (2 Marks)​

  • Journal: Computers and Electronics in Agriculture (Elsevier, Q1).
  • Justification: Top-ranked Q1 journal dedicated specifically to computer vision and embedded electronics in farming, with an Impact Factor ≈7.7\approx 7.7 guaranteeing high global readership and academic citations.