Hyperspectral imaging of a grapevine in a vineyard, showing water content distribution.

Is Your Vineyard Thirsty? How Tech Can Tell You What Your Grapevines Need

Mira Elwood in Tech & Innovation August 2025 • 4 min read.

"New research uses hyperspectral imaging and gray-level texture analysis to optimize irrigation for grapevines, ensuring healthier plants and better yields."

For vineyard managers, few things are as critical as proper irrigation. Too little water, and you risk stressing your grapevines, reducing fruit yield and quality. Too much, and you can cause a host of other problems, potentially leading to plant death. Water stress, indicated by closed stomata and restricted transpiration, is a common issue that directly impacts fruit production and its internal characteristics. But how do you know exactly how much water your vines need?

Traditional methods, such as pressure bombs and leaf porometers, are time-consuming and not practical for large-scale vineyards. Infrared thermography, which measures canopy temperature, is another option, but it requires additional environmental data to calculate crop water stress. Light interception methods, while useful, only provide indirect indications of water status. These methods often fall short of providing precise, real-time data that can drive informed irrigation decisions.

Fortunately, innovative technologies are stepping in to fill this gap. Recent research leverages hyperspectral imaging to quantify water content and detect water stress in plants with greater accuracy and efficiency. By combining reflectance data with advanced texture analysis, vineyard managers can gain a more comprehensive understanding of their plants' needs, leading to optimized irrigation and healthier, more productive grapevines.

Hyperspectral Imaging: A Clearer Picture of Vine Hydration

Hyperspectral imaging of a grapevine in a vineyard, showing water content distribution.

Hyperspectral imaging measures the reflectance and gray-level texture features of grape leaf surfaces, providing detailed data that can be used to predict water content. This technology captures images across a wide spectrum of light, including visible and near-infrared (Vis/NIR) wavelengths. Because plant pigments like chlorophyll absorb light in the visible band and reflect it in the near-infrared band, changes in reflectance patterns can indicate water stress.

A study published in the Transactions of the ASABE explored this approach, using Vis/NIR hyperspectral imaging to develop predictive models for water content in grapevines. The researchers applied varied levels of water treatment to grapevines in a controlled field environment. They then used the reflectance and gray-level co-occurrence matrix (GLCM) texture features to build partial least squares (PLS) models for water content prediction.

Reflectance Data: Measures how much light is reflected by the leaf at different wavelengths.
Gray-Level Co-occurrence Matrix (GLCM): Analyzes the spatial relationship of pixels in an image to extract texture features.
Partial Least Squares (PLS): A statistical method used to build predictive models from complex data sets.

The results indicated that combining reflectance and GLCM texture features yielded the most accurate predictions. The model achieved a correlation coefficient (rp) of 0.900, a root mean square error of prediction (RMSEP) of 0.826, a bias of -2.213e-04, and a residual predictive deviation (RPD) of 2.084. These metrics demonstrate a significant improvement over models using reflectance or GLCM texture features alone, highlighting the power of this combined approach.

The Future of Vineyard Management

By integrating Vis/NIR hyperspectral imaging with GLCM texture analysis, vineyard managers can gain a more accurate and nuanced understanding of their vines' water needs. This technology offers a rapid, non-destructive way to assess plant health and optimize irrigation strategies. The ability to predict water content accurately can lead to healthier plants, improved fruit quality, and more efficient use of water resources, contributing to a more sustainable and productive vineyard. As technology advances, expect to see even more sophisticated tools that help growers manage their crops with precision and care.

About this Article -

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Everything You Need To Know

What is hyperspectral imaging and how does it work in vineyards?

Hyperspectral imaging is a technology that captures images across a wide spectrum of light, including visible and near-infrared (Vis/NIR) wavelengths. In vineyards, this technology is used to measure the reflectance of light from grape leaf surfaces. Because plant pigments like chlorophyll absorb light in the visible band and reflect it in the near-infrared band, changes in reflectance patterns can indicate water stress in the grapevines. This data, combined with gray-level texture analysis, allows vineyard managers to predict water content and make informed irrigation decisions.

How does gray-level co-occurrence matrix (GLCM) texture analysis contribute to assessing grapevine water content?

The Gray-Level Co-occurrence Matrix (GLCM) is used to analyze the spatial relationship of pixels in a hyperspectral image. It extracts texture features from the image, which, when combined with reflectance data, provides a more comprehensive understanding of the grapevine's water status. This combined approach allows for more accurate predictions of water content compared to using reflectance data alone, leading to more effective irrigation strategies. The GLCM helps capture subtle variations in leaf appearance related to water stress.

What are the limitations of traditional methods for assessing grapevine water needs, and how does hyperspectral imaging overcome them?

Traditional methods like pressure bombs, leaf porometers, and infrared thermography have limitations. Pressure bombs and leaf porometers are time-consuming and impractical for large-scale vineyards. Infrared thermography requires additional environmental data. Light interception methods provide only indirect indications of water status. Hyperspectral imaging overcomes these limitations by providing precise, real-time data on water content through the combination of reflectance data and gray-level texture analysis, enabling rapid, non-destructive assessment of plant health.

What are the key benefits of using hyperspectral imaging in vineyard management, and what impact does this have on grape quality?

The key benefits of using Vis/NIR hyperspectral imaging in vineyard management include the ability to accurately predict water content, leading to optimized irrigation strategies. This results in healthier plants, improved fruit quality, and more efficient use of water resources. By preventing water stress, which can lead to closed stomata and restricted transpiration, hyperspectral imaging helps maintain optimal internal characteristics of the fruit, improving the yield and quality of the grapes.

Explain the role of Partial Least Squares (PLS) models in the context of hyperspectral imaging for grapevines.

Partial Least Squares (PLS) is a statistical method used to build predictive models from complex data sets, like those generated by hyperspectral imaging. In the research, PLS models were built using reflectance data and gray-level co-occurrence matrix (GLCM) texture features to predict water content in grapevines. The results, measured by metrics like correlation coefficient (rp), root mean square error of prediction (RMSEP), bias, and residual predictive deviation (RPD), demonstrated the effectiveness of the PLS models in accurately predicting water content and thus, assisting in precision irrigation techniques.