Moisture stress assessment in rabi maize using multispectral sensor mounted on an unmanned aerial vehicle

Yalamareddy Kiranmai^1*Milind Prabhakar Potdar^1*Biradar D.P¹Kuligod V.B¹Aditya Kanade¹BRAJENDRA PARMAR²Vaibhav Malunjkar³

• ¹University of Agricultural Sciences Dharwad, Dharwad, India
• ²ICAR - Indian Institute of Rice Research, Hyderabad, India
• ³Mahatma Phule Krishi Vidyapeeth, Rahuri, India

Early detection of moisture stress in maize is vital for sustainable irrigation management, improved water-use efficiency, and stable yields under water-limited conditions. Accordingly, the present study aimed to (i) evaluate the sensitivity of vegetation indices for detecting water stress, (ii) analyze their relationship with kernel yield, and (iii) assess their correlation with the Crop Water Stress Index (CWSI). A field experiment was conducted during 2021–22 rabi season at the University of Agricultural Sciences, Dharwad, under nine irrigation regimes in a randomized complete block design (RCBD). Multispectral imagery from a MicaSense RedEdge Unmanned Aerial Vehicle (UAV) sensor was used to derive the Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil-Adjusted Vegetation Index (SAVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption in Reflectance Index (TCARI). Irrigation limited to the knee-high stage or omitted at tasseling and silking significantly reduced vegetation indices and kernel yield (ANOVA, p ≤ 0.05). NDVI, RDVI, SAVI, and OSAVI exhibited strong positive correlations with yield, confirming their effectiveness in capturing reductions in canopy vigour, chlorophyll activity, and photosynthetic capacity under stress. By contrast, TCARI increased with stress, reflecting its sensitivity to pigment degradation. CWSI derived from canopy temperature supported these spectral responses and highlighted the high vulnerability of reproductive stages to water deficit. Collectively, UAV-based multispectral indices, when combined with ground-based CWSI offer a robust framework for early stress detection and precision irrigation in maize. These results demonstrate the potential of remote sensing technologies to improve water-use efficiency and advance climate-smart agricultural practices in water-scarce regions.