Advanced Technologies to Aid Grain Analysis Process

Agriculture development programs in both developed and developing countries have attempted to minimize hunger, food insecurity, and malnutrition. The agricultural development strategy of several developing countries has been focusing on national and household food security. Moreover, developing countries have become more proactive in agricultural trade, particularly in import activities, over the last decade. This, in turn, improved the demand for grain analysis in the market. Market players are adopting advent technologies to enhance the grain quality testing process or grain analysis.

What is Aid Grain Analysis?
The quality of food grains is determined by grain analysis. The grain analysis is a method of determining whether or not grains have been contaminated with particular technologies. The quality, storage time, and distribution of food grains are all monitored on a regular basis. Increase in the globalization of grain trade and stringent food and feed safety and quality regulations are major driving factors in the grain analysis market. According to Allied Market Research, the global grain analysis market is anticipated to grow at a significant CAGR of 6.2% from 2021 to 2030.

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There are approximately 12 crore farming properties in India, which means this immense variety of crops must be examined, with the number increasing if there are diverse varieties of crops. So, when farmers bring their products to wholesale marketplaces for quality testing, the problem is that they can't spend 30 minutes checking and evaluating each commodity. This is a growing problem in numerous countries and offers a lucrative opportunity for the market players and new entrants.

AI to expedite food grain quality testing
Nebulaa, an India-based start-up, has developed an AI-based grain analyzer to eliminate the exploitation of poor farmers. This firm is attempting to reinstate the food quality people eat to a higher standard by altering traditional ways with which food grains along with other agricultural products are graded at their origin so that the public can continue to eat higher-quality food than ever before while ensuring that farmers are not treated unfairly. MATT, an AI grain analyzer, offers a revolutionary new approach to food grain quality testing and generates a quality analysis of the product in under a minute.

This MATT technology uses deep learning and machine learning to deliver results 20 times faster than a trained human analyst. The MATT's performance is faster and is significantly more consistent and repeatable than any human tester. The primary testing is completed with the multiple images captured from numerous inbuilt cameras at different wavelengths, and the grain profiles are mapped on the basis of algorithms. After that, an AI 3D depiction of each grain is assessed for fungal damage, defects, tearing, organic impurity, and other issues, and a mathematical formalism is developed for each grain type. This is how MATT technologies can assist with food grain quality testing and have the ability to transform the way basic machine-testing of grain crops is done around the world.

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NIR technology-based portable cereal quality detectors to aid the cereal testing
Cereals are inspected for quality throughout the entire process, from crop yields to consumption. Wet chemical analysis methods are the most common traditional grain quality detection methods, which are labor-intensive, time-consuming, and extremely subjective, and low accurate, and pollute the environment. To increase the efficiency and accuracy of crop identification, scientists from the Chinese Academy of Sciences (CAS), Hefei Institutes of Physical Science (HFIPS) - developed a series of portable cereal quality speedy detectors based on NIRS (near-infrared spectroscopy).

The equipment used the NIR technology to identify cereal quality, such as storage time, edible quality, protein data, fat, amylose, and moisture content of cereal, which quickened the buying, storing, and transporting process. This device includes a number of detection features that are tailored to domestic food production settings and only takes 20 seconds to provide all the required data. The company further aims to reduce the instrument's cost and optimize its structure through more miniaturization.

Covid-19 impact
Due to supply chain interruptions, the Covid-19 pandemic had a moderate effect on the global grain analysis market. However, the removal of lockdowns is anticipated to aid the industry's growth in the future years. The market is expected to witness significant new launches, which will improve the grain analysis process, and the industry will recoup gradually.

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