Assessing Crop Health: The Advantage of Hyperspectral Imagery

A whopping 40% of the world's crop production succumbed to plant diseases in 2021, which cost the global economy over $220 billion annually, according to the Food and Agriculture Organization (FAO). When considering the impact of notorious invasive pests such as bark beetles and red palm weevils, that stirs up an additional cost of $70 billion in damages.

The urgency for innovative solutions to mitigate widespread crop losses due to plant diseases and pests is more pressing than ever.

Traditional monitoring methods, often reliant on ground visits, visual cues, or simplified indices, may not capture the intricate details needed for precise intervention. While satellite imagery emerged as a useful tool for monitoring crop health, conventional multispectral imagery has limitations such as a fixed number of spectral bands and hence fewer details and difficulty in differentiating similar materials. MSI is also sensitive to atmospheric conditions.

In recent years, hyperspectral imagery has emerged as a powerful tool, offering a nuanced view into the health of crops.

What is hyperspectral imagery?

Hyperspectral imaging, or HSI, is an advanced form of remote sensing, surpassing conventional methods such as RGB or multispectral imaging (MSI). Unlike their multispectral counterparts, hyperspectral imaging sensors record a continuous spectrum across the visible and near-infrared spectrum. Each pixel in the hyperspectral image corresponds to a unique spectral signature, providing a detailed and comprehensive view of the crop's health.

RGB (red, green, blue) imaging captures only three bands of visible light; multispectral imaging builds upon conventional RGB imaging by capturing non-visible components of the electromagnetic spectrum; hyperspectral imaging acquires and processes information across the electromagnetic spectrum for each pixel in an image

Until recently, the integration of satellite-based hyperspectral imagery was dominated by government-led space missions. However, a notable shift is being marked by the rise of private startups such as Pixxel, pioneering new avenues in the spaceborne HSI landscape.

The precision of hyperspectral imagery

Hyperspectral imaging sensors can spot the tiniest changes – invisible to the human eye – in how the crops reflect light. These sensors can capture spectral properties within the visible, near-infrared, and shortwave infrared regions, spanning from 400 to 2,500 nm.

Plants under stress exhibit distinct spectral signatures. By studying the reflected light across numerous wavelengths, analysts can detect subtle changes in crops that might indicate early signs of diseases, nutrient deficiencies, or environmental stress. The sensors can also capture the spectral reflectance of pests on crop surfaces, providing a detailed assessment of the damage level.

HSI principles diagram (Image credit: Lincoln Library, Journal 4) Source: EOPortal

Monitoring crop health through hyperspectral imagery

Hyperspectral imagery proves invaluable in monitoring crop health, offering applications including the detection of early stress symptoms, identification of diseases, and quantification of severity.

For instance, in viticulture, hyperspectral imagery can distinguish between healthy and stressed vines, even before visible symptoms emerge. Identifying these subtle changes facilitates prompt action, preventing the spread of diseases and optimising the overall yield. The granularity of hyperspectral imagery data ensures that farmers can diagnose specific issues, whether a fungal infection or a nitrogen deficiency, with unprecedented accuracy.

Hypercubic architecture hyperspectral images of a vineyard canopy. Source: Pixxel whitepaper

Prof Cory Hirsch at the University of Minnesota is leveraging hyperspectral imagery to detect diseases, including sudden death syndrome (SDS) and brown stem rot, which impact soybean yields.

Numerous studies have showcased the effectiveness of hyperspectral imaging in successfully detecting various pathogens in both living plants and harvested crops. For example, the detection of Fusarium Head Blight (FHB) in different developmental stages of wheat plants under laboratory conditions has been demonstrated using hyperspectral imagery. One study, in particular, showcased the ability to identify FHB-affected wheat grains with 91% accuracy through hyperspectral imagery. Additionally, HSI has proven effective in the early detection and quantification of infections in other pathosystems, such as barley-brown rust and grapefruit-citrus canker.

Advantages of hyperspectral imaging over traditional methods

Comparing hyperspectral imagery to traditional methods reveals significant advantages. Visual inspection or manual detection is time-consuming, subjective and often not satisfactory since it depends on human abilities. Manual detection relies on the manifestation of clearly visible symptoms of diseases or stress, typically occurring in the mid-to-late stages of infection. Further, diseases often initiate in small regions of the foliage, where visual inspection may prove challenging, especially in large crops.

Wheat canopy affected by Septoria leaf blotch during later stages of development. Source: Pixxel whitepaper

While multispectral satellite imagery provides a broader perspective than in-situ methods, the technology lacks the precision and specifics offered by hyperspectral imagery.

The ability to identify diseases at this early stage presents an opportunity for early intervention. This may involve measures to control and prevent the spread of infection or adjust crop management practices before the entire crop succumbs to infection. Recognising crop areas affected by disease also opens up options for targeted application of chemicals, optimising the usage of pesticides and herbicides.

Challenges with hyperspectral imagery and solutions

While the potential of hyperspectral imagery in agriculture is vast, there exist challenges such as complex data interpretation, initial cost barriers, and the need for seamless integration. Ongoing efforts focus on simplifying data interpretation through user-friendly interfaces and automated analysis tools, with an expected decrease in costs as technology advances. Successful adoption depends on collaborative efforts among stakeholders, including technology developers, researchers, and agronomists.

Pixxel's hyperspectral imaging satellites

Pixxel's upcoming hyperspectral imaging satellites offer a revolutionary solution for advanced agricultural monitoring, providing high-resolution images, precise spatial accuracy, and frequent revisits. As the Pixxel constellation reaches full operational capacity, it will equip stakeholders in the agriculture sector with invaluable information, enabling optimised practices for sustainable food production.

Beyond enhancing agricultural outcomes, the adoption of Pixxel's satellite-based hyperspectral imaging solutions reflects a commitment to a more sustainable and environmentally conscious future.

Connect with their sales team for a deeper insight into the potential benefits of Pixxel's cutting-edge technology for your organisation.

FAQs

What is hyperspectral imagery, and how does it differ from traditional imaging methods like RGB or multispectral imaging?

Hyperspectral imagery is an advanced form of remote sensing that records a continuous spectrum across the visible and near-infrared spectrum. It differs from RGB or multispectral imaging by capturing a detailed and comprehensive view of crop health, providing unique spectral signatures for each pixel in the image.

How can hyperspectral imagery help in monitoring crop health?

The precision and intricate details captured by hyperspectral imagery are invaluable in agriculture, enabling the early detection of stress symptoms, identification of diseases, and quantification of their severity. This enables prompt action and optimization of overall yield by distinguishing between healthy and stressed crops, often before visible symptoms emerge. This proactive approach is a game-changer in precision agriculture, allowing for targeted interventions and sustainable crop management practices.

What advantages does hyperspectral imaging offer over traditional methods in agriculture?

Hyperspectral imaging provides significant advantages over traditional methods. It surpasses visual inspection or manual detection, offering speed and precision in early disease identification. It also outperforms multispectral satellite imagery in terms of specificity and accuracy, allowing for targeted applications of chemicals and optimised pesticide and herbicide usage.

What challenges does hyperspectral imagery face, and how are they being addressed?

Challenges in the use of hyperspectral imagery in agriculture include availability, initial cost barriers and intricacies of data interpretation. Ongoing efforts focus on simplifying data interpretation through user-friendly interfaces and automated analysis tools, including machine learning models. As technology advances, the availability and cost of acquiring hyperspectral imagery are expected to decrease, making it more accessible and affordable.

How is Pixxel's hyperspectral imaging technology contributing to advanced agricultural monitoring?

Pixxel's fleet of hyperspectral imaging satellites provides high-resolution images, precise spatial accuracy, and frequent revisits. As the constellation reaches full operational capacity, it will equip stakeholders in the agriculture sector with invaluable information, enabling optimised practices for sustainable food production. The technology reflects a commitment to a more sustainable and environmentally conscious future.

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