National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Agricultural drone technology is increasingly positioned as a component of future-ready crop production, yet its practical contribution depends on more than aerial coverage or sensor sophistication. This narrative review critically synthesizes evidence published mainly between 2012 and July 2026 on unmanned aerial vehicle (UAV) platforms, visible, multispectral, thermal, hyperspectral and LiDAR sensing, imagery-to-decision workflows, precision spraying and integration with artificial intelligence (AI) and the Internet of Things (IoT). Applications span crop establishment, phenotyping, water and nutrient assessment, weed and disease surveillance and site-specific input delivery. Across these applications, the distinction between detecting a spatial anomaly and diagnosing its agronomic cause is fundamental: spectral responses may reflect several interacting stresses, and high classification accuracy does not necessarily establish treatment value. For spraying, deposition, drift, biological efficacy, label compliance and operator competence determine performance more directly than coverage speed. The review evaluates practical barriers including platform endurance, calibration, algorithm transferability, data governance, fragmented holdings and uncertain economic returns. India provides an instructive context through its drone regulatory framework and service-oriented programmes, but institutional support must be complemented by locally tested protocols and independent evidence. A decision-centred adoption framework is proposed that links the management question, appropriately selected UAV system, ground validation, actionable recommendation and post-intervention assessment. Future research should prioritize multi-location validation, transparent AI, crop-specific spray testing and realistic comparison of custom-hiring, cooperative and ownership models. The central conclusion is that drones can support sustainable crop management when their outputs are agronomically credible, economically justified and responsibly deployed.
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