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How Spectral Data Can Transform Agri-Trade

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How Spectral Data Can Transform Agri-Trade

Agritech services based on spectral sciences to pave the way to advanced and transformed Agri-trade

With the rapid advancements in Agricultural technology, the sector is witnessing new waves of tech-fueled progress and innovations that have put Indian Agriculture on the Global Agritech map. Today, the Indian Agritech vista is reaping the benefits of digitization by embracing a plethora of new-age technologies like AI, ML, Big Data, IoT, etc.

The widespread adoption of deep technology is opening up a new frontier for agriculture in post-harvest systems. Let’s take wheat, for instance, the moisture content, dry gluten, starch, etc. are essential factors to assess the nature and endurance of the commodity. These factors determine the prices during market transactions. However, during manual evaluations of quality, the process of certifying the exact quality of the wheat crop is a hard and subjective method that results in additional surcharges for both buyers and sellers. Additionally, the conventional quality checking methods are time-consuming and cost inefficient.

This is where next-gen Agtech technologies like IR-Spectroscopy come into play. Infrared Spectroscopy enables agriculture researchers to learn and discover the various properties of a food commodity by illuminating the specimen with a given light which when reflected back reveals the essential characteristics of the said specimen. Spectral data can aid in the creation of a quality map for key commodities, allowing processors to drive procurement efficiency through geo-varietal and process optimisation. Discovered by the Great Indian Scientist and Nobel Laureate C.V Raman, this revolutionary technology has recently become prominent in the field of agriculture. The results have been phenomenal, to say the least. This technology is being leveraged across the post-harvest agriculture landscape for quality assessment processes which can reveal the purity and important statistics of any harvested crop.

Leveraging the latest technologies such as AI/ML can work wonders in such a situation. The system has helped effortlessly remove all contingencies of manual errors by eliminating the scope of subjectivity from the process. Existing NIR technology is combined with AI/ML to increase accuracy as the system gains experience. Some technologies used in AI/ML are deep learning, Edge Inferencing, Artificial Intelligence Accelerators, etc. This utilization of AI-based spectroscopic technology will prove instrumental in rapid quality testing, lowering the testing time to less than 1 minute, while warranting that all potential holdups during the quality reporting process cease to exist. By leveraging this technology, a slew of physical and chemical factors that impact trade decisions can be ascertained with ease. Overall, digital, rapid, and accurate quality assaying solutions make mapping quality across the value chain easier.

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A number of new-age Agtech startups are helping various stakeholders in the food value chain by saving costs in the procurement of raw materials through rapid quality assessment technology. Conventionally speaking, Agritech businesses have always depended on individual crop-wise manual expertise for precisely determining the quality of products. Spectroscopy can significantly resolve a number of issues in post-harvest agricultural processes by eliminating biases and standardizing the entire operation through technology by measuring various chemical quality parameters of numerous food commodities such as grains, animal feed, and milk. This technology can prove valuable in conducting fast-paced adulteration testing that can considerably ensure food safety standards and build trust in the end user.

Agritech businesses are leveraging a number of novel spectroscopic tools to determine the quality of food grains before they reach our pantries. In the post-harvest supply chain, IR spectroscopy is used for Gram, Wheat, Moong, Soybean, Paddy, Maize, Chili, Rice, Toor dal, Urad, Bajra, Mustard, Chana Dal, Turmeric, etc.

These are also utilized for rapid quality assessment of key chemical parameters of food commodities across Grains, Oilseeds, Pulses, Spices, and Beverages segments. Key commodities include Wheat and its derivatives, Rice, Paddy, Maize, Mustard, Turmeric, and Chilli Powder as well as animal feed commodities like Soybean Meal, Cottonseed Meal, Mustard Meal, Mustard Cake, De-Oiled Rice Bran, DDGS, RiceBran/Rice Polish,  ice Bran Meal and Huller Bran. There are also various Portable solutions that specialize in gauging the composition as well as conducting the adulteration testing of milk in less than 1 minute. The technology also offers a way for the rapid detection of adulterants like Urea, Salt, Detergent, Ammonium Sulphate, and Caustic Soda.

Agritech startup innovations are addressing some of the most pressing issues in agriculture and food products such as suboptimal food quality, wastage, lack of traceability etc. The Agritech frontier is evidently on the cusp of a revolution. The induction of deep tech has not only enhanced the output quality but has also built trust and transparency across multiple post-harvest food value chains. The post-harvest agtech industry has a great deal of potential to be redefined and reshaped by technologies like IR spectroscopy. The potential for blockchain and spectral data integration is enormous. It can provide 100% traceability and foster trust in the value chain. This technology is transforming the food and agriculture sectors by improving organizational decision-making capabilities.

Despite the fact that technology has advanced substantially, the future lies in the managed services for the stakeholders at procurement, warehousing, processing, and distributors. We need agritech firms that can offer comprehensive inspection and fulfillment services based on spectral sciences to pave the way to a more advanced and transformed Agri-trade.

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Author:

Taranjeet Singh Bhamra, CEO & Founder, AGNEXT

The post How Spectral Data can Transform Agri-Trade appeared first on .

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