In the agricultural realm, image analysis plays a pivotal role in understanding crop health, detecting issues, and making data-driven decisions. Among the critical factors that significantly impact the effectiveness of image analysis is the management of contrast and brightness. In this blog post, we will delve into the significance of contrast and brightness in agricultural image analysis and how they enhance the quality and usability of agronomic information.

Understanding Contrast and Brightness

Contrast within an image refers to the variation in brightness or color between different parts, creating visual distinctions that capture the viewer's attention. It is a key element in visual composition, facilitating the differentiation of various elements. In agricultural image analysis, contrast is essential for accurately identifying and interpreting crop health, disease symptoms, stress indicators, and other important characteristics.

To monitor and comprehend contrast in an image, a histogram is a commonly used visual tool. The histogram represents the distribution of brightness or color levels throughout the image. When considering brightness contrast, the histogram provides insights into how brightness values are distributed across the tonal scale, ranging from the darkest tones (blacks) to the brightest tones (whites). A well-balanced brightness histogram exhibits an extended distribution across the tonal scale, indicating good contrast within the image.

Example of an histogram graph representing the light distribution of an image.

The Impact of Contrast on Phenotyping

Phenotyping, the process of measuring and analyzing plant traits, heavily relies on accurate and detailed imagery. Having well-contrasted images for phenotyping is crucial for a comprehensive interpretation of the information contained within each pixel. By accessing the full color range of an image, the richness of information increases, enabling the extraction of valuable agronomic insights. Thus, the quality of the information derived from crop images is highly dependent on the quality of the provided images.

Depending on the specific traits of interest or desired outputs, the level of contrast needed in images may vary. At Hiphen, we specialize in accompanying and guiding our clients in defining the exact level of contrast required for optimal data processing and a seamless phenotyping experience. By fine-tuning contrast levels, we enhance the accuracy and reliability of trait analysis, ultimately empowering better decision-making for researchers.

Image with an overall bad contrast


Image with an overall good contrast

The Challenges of Poor Contrast

Working with poorly contrasted images for phenotyping poses challenges and hinders the process. Images with inadequate contrast may be interpreted as underexposed or overexposed, impacting the processing of traits such as green cover, vegetation indices, disease detection, and organ segmentation among others from Hiphen's portfolio. Dealing with such images becomes time-consuming, tedious, and often results in data processing delays, leading to late delivery of results.

At Hiphen, we are constantly improving the automation of data quality checks through our Cloverfield™ platform. By the time you upload your datasets, we can promptly identify if the data is likely to generate processing issues or not. This allows us to inform our clients as soon as possible, in a fully transparent way and via an interactive dashboard, to ensure a frictionless phenotyping experience and to minimize delivery delays caused by poorly contrasted images.


The management of contrast and brightness is of utmost importance in agricultural image analysis. By understanding and optimizing contrast levels, we unlock the full potential of color information, facilitating the extraction of valuable agronomic insights. At Hiphen, we excel in developing AI methodologies for agricultural applications, assisting our clients in making informed decisions and adapting to ever-changing environmental conditions. With our Hiphen Academy, the first e-learning platform dedicated to help acquiring research-grade imagery from drones, we provide the necessary knowledge and skills to enhance image quality and maximize the potential of agricultural image analysis from the PhenoScale product range. Through effective contrast and brightness management, we can improve agricultural practices using digital phenotyping and drive innovation in the industry.



Your Hiphen Team.
Topic brought to you by Martin GIRARDEY- Image Processing Specialist.