Hyperspectral Imaging And Agriculture (Or How To Pick The Perfect Pepper)
By John Oncea, Editor
A method of classifying the ripeness of peppers using hyperspectral imaging was in the news recently but the technology has been a part of the agriculture industry for decades. Learn more about this non-invasive technique that can determine a pepper's firmness and a whole lot more.
Have you ever had a tough time picking the right pepper at a supermarket?
Peppers come in different shapes, sizes, and colors with over a thousand different types to choose from. That makes selecting the perfect pepper difficult, writes Ward’s Supermarket. To that point, Ward suggests you “choose peppers with deep, vivid colors. For example, a dark green pepper will be riper than a lighter shade.
“Also, see that there are no bruises, blemishes, or sunken areas, as this may indicate overripeness. You’ll want a pepper that is well-shaped and glossy without wrinkled skins!” Other tips include avoiding peppers that are not firm to the touch (a gentle squeeze will let you know if the skin feels right) and be sure to pick them up as heavier peppers are usually riper, thick-walled, and juicy inside.
Or, you could follow the University of Granada’s lead and employ hyperspectral imaging to identify, classify, and visualize the quality attributes of your pepper – or any other fruit and vegetable.
Peter Piper Picked A Peck Of Pickled Peppers
A team of researchers from the University of Granada’s (UGR) Color Imaging Lab, in collaboration with the Department of Analytical Chemistry and the Hortofrutícola Mabe cooperative in Almería, has developed a new method for measuring the ripeness of peppers, UGR announced. This method aims to prevent the packaging of overripe peppers and was developed using the California-type pepper, which is typically blocky in shape and has thick flesh.
The project makes it possible to determine the firmness of the pepper — a quality linked to its ripeness that is key to its success on the market – by employing hyperspectral imaging.
“The scientists analyzed spectral reflectance in the visible and near-infrared range to determine the ripeness of peppers from three different crops, identifying the spectral bands that provide the best classification rates for gauging ripeness,” writes UGR. “The project has constructed a realistic scenario, simulating a conveyor belt system, in which the peppers are assessed using four classification algorithms to predict their ripeness.”
The classification process used an algorithm that achieved success rates of over 90%, something made possible by a system that estimates the shelf-life of peppers and ensures better product quality for customers. The system provides an effective and practical solution for classifying the ripeness of peppers, which can help fruit and vegetable growers improve their crop management and reduce losses caused by overripe products. The system is flexible and allows companies to choose the number of spectral bands that suit their budget and the type of product they want to analyze.
This is one of the latest examples of how hyperspectral imaging is advancing precision agriculture, enhancing crop management practices, and contributing to sustainable and efficient farming techniques.
A Brief History Of Agriculture
Agriculture took root about 12,000 years ago and spurred humankind’s transition from nomadic hunter-gatherer lifestyles to permanent settlements and farming. “Out of agriculture, cities and civilizations grew, and because crops and animals could now be farmed to meet demand, the global population rocketed — from some five million people 10,000 years ago, to eight billion today,” writes National Geographic.
There was no single factor or combination of factors that led people to start farming in different parts of the world. In the Near East, it is thought that climatic changes at the end of the last ice age brought seasonal conditions that favored annual plants like wild cereals. In other regions such as East Asia, increased pressure on natural food resources may have forced people to find homegrown solutions. Regardless of the reasons for its independent origins, farming laid the foundation for the modern era.
As farming took hold around the world tools were developed and improved with digging sticks and millstones used during the Neolithic period examples of some of the earliest agricultural tools, according to Quora. The Bronze Age saw the introduction of plows, sickles, scythes, irrigation tools, and winnowing baskets, with animal-drawn carts and wagons added to the farming mix along the way.
The Romans added seed drills and, during the Medieval period, ox-drawn plows became more widely used. The 18th and 19th centuries brought about significant advancements in farming technology, including the use of horse-drawn cultivators and seed drills.
Industrial agriculture, a form of modern farming that refers to the industrialized production of crops and animals and animal products like eggs or milk, began in the 1800s and 1900s and is a result of social and technological processes, writes InTeGrate. The goal of industrial agriculture is to increase agricultural yields for growing populations. This is done by using fossil fuels, mechanization, and advanced crop breeding methods.
The industrialization of agriculture happened quickly, with more changes occurring in the 20th century than in previous agricultural history. This process was aided by the invention of synthetic fertilizers and pesticides in the early 20th century. Industrial agriculture has become so prevalent that sustainable farming practices are sometimes called “alternative.” Since the 1960s, agriculture has been dominated by large-scale multinational corporations.
Hyperspectral imaging was developed by NASA’s Jet Propulsion Laboratory in the late 1970s and the agriculture industry was quick to adopt it. Since then, it has become an important tool, significantly extending the range of farming issues and applications that can be addressed using remote sensing.
“Almost every farming issue (weeds, diseases, nutrient deficiency, etc.) changes the physiology of the plant, and therefore affects its reflective properties,” writes Medium. Healthy crops and crops that are affected by disease reflect the sunlight differently. Using hyperspectral imaging it’s possible to detect very small changes in the physiology of the plant and correlate it with the spectrum of reflected light.”
Increase Yields While Reducing Water Usage And Chemical Inputs
The advancement of technology in agriculture is crucial in assisting farmers with decision-making related to crop health and resource management. The use of hyperspectral imagery in precision agriculture is an effective method that aids farmers in using resources like herbicides and pesticides.
“Hyperspectral imaging has proven to be a valuable technology in agriculture, particularly in the domains of vegetation analysis and precision farming,” writes Specim. “It provides a deep understanding of crop health and environmental factors enabling more efficient and sustainable agricultural practices.”
When used in agriculture, hyperspectral imaging captures and analyzes information from the electromagnetic spectrum beyond what the human eye can perceive. Sensors capture data across multiple spectral bands, allowing for detailed analysis of objects or materials based on their spectral signatures.
Specim singles out several benefits to agriculture provided by hyperspectral imaging, including increased yields, reduced resource usage, early detection of plant diseases, increased nutrient and water efficiency, more accurate yield prediction, reduced use of chemicals, reduced weed-related losses, lowered manual inspection costs, better yield quality and higher profits, and minimized environmental impact.
Because it is a non-destructive, contactless technology, hyperspectral imaging can be applied in agriculture in many ways including:
- Crop Health Monitoring: Hyperspectral imaging can be used to assess the health of crops by analyzing their reflectance and absorption patterns. This helps in the early detection of diseases, nutrient deficiencies, and stress conditions.
- Precision Agriculture: By providing detailed spectral information, hyperspectral imaging enables precision agriculture practices. Farmers can optimize the use of resources such as water, fertilizers, and pesticides based on specific crop needs.
- Disease Detection: Early detection of plant diseases is crucial for preventing their spread and minimizing crop losses. Hyperspectral imaging can identify subtle changes in plant spectral signatures associated with diseases, allowing for timely intervention.
- Weed Detection and Management: Hyperspectral imaging can be utilized to differentiate between crops and weeds based on their spectral characteristics. This aids in the development of targeted weed control strategies, reducing the need for herbicides.
- Crop Classification: The technology can be used to classify different types of crops and assess their spatial distribution in a field. This information is valuable for crop monitoring and management.
- Yield Prediction: By analyzing spectral data over time, hyperspectral imaging can contribute to predicting crop yields. This information is beneficial for farmers and agribusinesses in planning harvest and marketing strategies.
- Environmental Monitoring: Hyperspectral imaging also can be applied to monitor environmental factors such as soil composition, moisture content, and overall environmental conditions, providing insights into sustainable agriculture practices.
- Remote Sensing: Hyperspectral sensors on satellites or drones enable remote sensing of large agricultural areas. This facilitates efficient monitoring and management of crops on a larger scale.
Hyperspectral imaging plays a crucial role in increasing yields and reducing water usage and chemical inputs. As the technology becomes more affordable and accessible, it will play an even bigger role in mapping yield estimation, irrigation scheduling, soil characterization, and pest detection. And, as with the technologies that preceded it, hyperspectral imaging will only prove more valuable as the farmers using it develop more innovative applications in the years ahead.