Beyond visible light: what is multispectral imaging?
Traditional scouting relies on visual cues, but plant stress often manifests before it is visible to the naked eye. Multispectral sensors capture narrow bands of light across the visible and near‑infrared spectrum. Healthy leaves reflect large amounts of near‑infrared (NIR) light, whereas stressed vegetation reflects less. Researchers organize these reflectance ratios into vegetation indices. Wingtra notes that common indices include the Ratio Vegetation Index (RVI) and the ubiquitous Normalized Difference Vegetation Index (NDVI). The red‑edge region between red and NIR wavelengths is particularly sensitive to early stress; the Normalized Difference Red Edge (NDRE) index detects subtle changes in chlorophyll.
Case study – early‑stage lettuce monitoring
Inspired Flight’s 2026 white paper demonstrates how multispectral data guides management decisions. Using a Sentera 6X multispectral sensor on an IF800 Tomcat drone, the team flew over an 8.47‑acre baby‑lettuce field in California for 7 minutes. The mission captured high‑resolution RGB and multispectral images, producing an orthomosaic and spectral maps. The sensor’s 20 MP RGB camera revealed gaps and uneven growth, while the multispectral data focused on the Chlorophyll Index Green (CIG). CIG compares NIR and green reflectance to measure chlorophyll content, which correlates with nitrogen levels and photosynthetic capacity. Areas with low CIG signaled nutrient deficiency or water stress, prompting targeted intervention.
The flight also generated NDVI maps, showing that most of the field was healthy. By refining the NDVI histogram, the team calculated that 5.72 acres exhibited positive NDVI values, effectively quantifying biomass.
Interpreting vegetation indices
The power of multispectral data lies in translating spectral values into actionable metrics:
- Chlorophyll Index Green (CIG) – Highly responsive during early growth, CIG reveals variations in chlorophyll and nitrogen. High values indicate vigorous growth, while low values highlight stress caused by nutrient deficiency, water scarcity or disease.
- Normalized Difference Vegetation Index (NDVI) – NDVI measures the difference between reflected NIR and absorbed red light. High NDVI values correspond to dense, healthy vegetation; low values reveal sparse or stressed crops.
- Red edge indices (NDRE) – The red‑edge band is highly sensitive to chlorophyll changes. NDRE detects stress earlier than NDVI, enabling preventative action.
Practical applications for yield decisions
Multispectral data goes beyond pretty maps. It drives concrete management actions:
- Targeted fertilization – CIG maps identify nitrogen‑deficient zones so that farmers can apply fertilizer precisely where needed, cutting costs and avoiding over‑application.
- Optimized irrigation – By highlighting water‑stressed areas, spectral data supports variable‑rate irrigation. Seeing how plants respond to water across a field helps farmers reduce waste and spot leaks.
- Early pest and disease detection – Low chlorophyll or abnormal spectral patterns may indicate pest infestations or diseases. Early detection allows swift intervention before the problem spreads.
- Weed and yield mapping – Multispectral sensors isolate weeds because they reflect light differently than crops. Timely weed removal prevents competition and reduces herbicide use. NDVI and CIG also enable yield estimation by calculating biomass.
- Insurance and documentation – Spectral maps provide high‑accuracy evidence for insurance claims after storms or disasters.
Why drones are superior to satellites
While satellites have provided multispectral data for decades, they suffer from coarse spatial resolution, infrequent revisits and cloud cover. Drone‑mounted sensors achieve centimeter‑level spatial resolution, customizable flight schedules and flexible spectral resolution. Farmers can capture data whenever needed and at scales appropriate for precision interventions.
Conclusion
Multispectral drones offer farmers a powerful window into plant health. By measuring subtle changes in reflectance, they reveal nutrient deficiencies, water stress, pests and weeds well before symptoms appear. Targeted fertilization and irrigation save input costs, while early interventions protect yield and reduce environmental impact. As sensors and analytics improve, multispectral imagery will become an integral part of data‑driven agriculture.