Hyperspectral imaging is a powerful tool for analyzing the chemical composition of agricultural products, but it has been hindered by its complexity and high cost. However, a team of researchers from the University of Illinois Urbana-Champaign has developed a game-changing solution – a deep learning-based method to reconstruct hyperspectral images from standard RGB images. This innovative approach could revolutionize product assessment in the agricultural industry, making chemical analysis accessible and affordable. By leveraging deep learning algorithms, the researchers have found a way to extract detailed chemical information from simple RGB images, opening up new possibilities for quality control and optimization across various agricultural sectors.
Accessing the Super Power of Deep Learning
The magic ingredient in this revolutionary model is the use of deep learning algorithms. The researchers used deep learning models to train and—to a certain extent—transform the information in RGB images into hyperspectral format, so as to connect the gap between scarce data in conventional SLC images and rich chemical information present in hyperspectral imaging.
It is a process for mapping and recreation of wavelength region between 700 nm and 1,000 nm (outside radiance range in visible spectrum captured by RGB images). Meaning, we can identify the very important chemical traits previously unattainable through standard RGB assesment. They have shown the possibility of this method by getting over 70% accuracy for soluble solid content and around 88% accuracy in dry matter on sweet potato, which is a tremendous improvement compared to previous work.
This is expected to revolutionise the evaluation of agricultural produce by providing farmers, processors and retailers with inexpensive handheld devices to access complex chemical information, rather than large and costly hyperspectral imaging laboratory systems.
Uber, Google Here, and Lyft — Simplifying Quality Control in a Whole Sector
This form of imaging benefit presented by deep learning puts in place the possibility to significantly simplify the analytical paths. There is no requirement for expensive hyperspectral imaging equipment, but standard RGB images obtained easily from camera phones or cameras are enough.
This availability makes it accessible to everyone and also facilitates more widespread use in agriculture. This availability of accurate, chemical details on agricultural products now gives farmers, processors and even end-consumers the ability to make informed decisions and optimize product quality.
This includes evaluating the soluble solid content and dry matter of sweet potatoes so that growers can manage their crops better, deliver a consistent high-quality product to both processors and consumers. Until now, only part of the agricultural value chain has had access to this level of understanding, as it required specialized research settings.
In addition, the authors have theorized that such an approach could be used in egg and hatchery-based industries, where–not too different from Matthew’s maggot-based predictions–they may better predict chick embryo mortality. It is an example of the wide future applications of this pioneering imaging methodology that can reshape quality control and decision-making strategy within several agricultural sectors.
Overcoming Challenges and Shaping the Future
Although this deep learning-based imaging method has clear implications, the researchers say that there are still issues to resolve before it can be adapted in an industrial scale.
There are many challenges to overcome including the fact that deep learning algorithms must be further developed and benchmarked on a wide variety of agricultural products and applications. Researchers have come a long way but will need to further refine and test their models to build trust in them so they can be used on a wider scale.
Moreover, the incorporation of this technology into current agricultural methodologies and decision-making practices will need discerning thoughts and liaison between agroindustry partners as well These results will only have real-world impact if integration is seamless, and user experiences are themselves commonplace.
Nevertheless, the researchers are upbeat; and the project highlights that they want to see improved agricultural quality assessment. Through further innovation and the partnerships they have built within industry, they believe that this technology could transform how we manage product quality in a way which would benefit farmers, processors and consumers alike.
As the agriculture of tomorrow continues to take shape and the needs of food production grow, this imaging solution powered by deep learning demonstrates how advanced technology can be harnessed to tackle challenges and pave a way toward a more sustainable, productive and just future for our industry.
