Is Your Garden Thirsty? Unlock the Secrets of Crop Water Status with Thermal and Optical Imaging

The Crop Water Stress Index (CWSI) is calculated from thermal images and is an indicator of the water stress level a plant is experiencing. A higher CWSI value generally means the plant is under more water stress. The experiment found that the CWSI is a particularly reliable indicator of water stress, even when nitrogen levels vary. If you cannot measure the CWSI directly, understanding environmental conditions such as temperature and humidity could help infer the relative water stress.

The Normalized Difference Vegetation Index (NDVI) is an optical index that reflects the health and chlorophyll content of plants. It is calculated using the red and near-infrared portions of the electromagnetic spectrum reflected by the plants. Higher NDVI values typically indicate healthier plants with more chlorophyll. The data suggests that NDVI, alongside other measurements, contributes to a more comprehensive understanding of plant water status and overall health.

The Photochemical Reflectance Index (PRI) is another optical index and it is sensitive to photosynthetic efficiency in plants, specifically changes in the xanthophyll cycle, which relates to how plants deal with excess light energy. While PRI offers insights, it's generally used in conjunction with other indices like CWSI and NDVI to gain a more complete picture of a plant's condition and response to different environmental conditions.

Thermal and optical imaging allows for non-destructive, rapid assessment of plant water status over large areas. This enables more efficient irrigation practices by targeting water application to areas where plants are showing signs of stress, which can conserve water and improve crop yields. It also facilitates research into plant responses to water availability, as data can be gathered quickly and repeatedly. Continuous monitoring using these methods can establish a baseline, which could show even slight deviations from ideal growing conditions.

The research involved two spinach varieties grown under varying water and nitrogen conditions in a greenhouse. Researchers used thermal cameras and hyperspectral sensors to gather data, which was then analyzed to correlate various indices (CWSI, NDVI, and PRI) with actual plant water status measured through stomatal conductance, chlorophyll fluorescence, and leaf water content. This allowed them to determine the reliability of each index in detecting water stress under different conditions. Future experiments could apply machine learning techniques on the datasets to improve prediction accuracy and robustness.