Masa Buden

The demand for increased food production and the expansion of saline soil areas highlights the need to study plant tolerance to elevated soil salinity. Soil salinity is a significant environmental stressor that limits crop growth and yield by reducing water absorption due to osmotic pressure. Additionally, it leads to nutrient imbalances, particularly calcium and potassium, due to the excessive presence of sodium and chloride ions. Various stress factors influence plant yield. Utilizing phenotyping techniques, particularly multispectral bands, can help in early detection and management of plant stress.

This study aims to evaluate plant responses to salinity using a handheld multispectral sensor Plant-O-Meter to develop automated phenotyping techniques for detecting salinity stress in basil plants. Main goal of this research is to assess plant response to salinity using a multispectral handheld sensor via vegetational indexes, and to potentially develop automated phenotyping techniques to detect early responses to salinity stress in basil plants. This study was conducted on Genovese basil (Ocimum basilicum L., var. Basilicum). It is widely cultivated for the production of essential oils and is also marketed as an herb, either fresh, dried or frozen. Basil, commonly cultivated in regions afflicted by drought and salinity, stands as a prevalent aromatic and medicinal herb. The convergence of drought and salinity stress in agroecosystems, alongside their similar symptoms on plants, poses a challenge in distinguishing between them. To assess the impact of soil salinity, basil plants were exposed to 40 mM NaCl (T2) and 80 mM NaCl (T3) concentration for 60 days during the 2024 growing season. Also, water control was used (T1). Plants were grown in pots in indoor growing conditions. Every treatment was carried out in three replicates. Handheld multispectral sensor was used to measure plant spectral reflectance. Three different vegetational indices (VI) were used to assess the plant response: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Renormalized Difference Vegetation Index (RDVI). Calculation of f-ratio values show significant differences between treatments for each calculated index. Using ANOVA for multiple comparison analysis testing shows significant differences between NDVI values of T1 and T2, and T2 and T3. Same results were calculated for RDVI. EVI values show significant differences between all three treatments. These results indicate potential usage of NDVI, EVI and RDVI in stress prediction in basil using remote sensing tools.

Audience Take Away:
Explain how the audience will be able to use what they learn?
How will this help the audience in their job? Is this research that other faculty could use to expand their research or teaching? Does this provide a practical solution to a problem that could simplify or make a designer’s job more efficient? Will it improve the accuracy of a design, or provide new information to assist in a design problem? List all other benefits.

• The audience can apply the findings to enhance early detection and management of salinity stress in basil plants, improving crop yield and quality.
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  Provides data for developing practical solutions for optimizing crop management practices and mitigating the impact of salinity stress on plant health.
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  Other faculty members can utilize this research to expand their teaching and research endeavors in plant physiology, remote sensing, and precision agriculture.
• The developed automated phenotyping techniques streamline data collection and analysis, offering a more efficient approach to monitoring plant health in agricultural systems.