The entry of AI has disrupted almost every field and agriculture is no exception. You might have read about all the advancements in the field of agriculture from hybrid seeds to digitized platforms for buying and selling of agricultural produce. Today, you will come to know about an addition to these- AI based grain quality assessment technology.
Let’s understand the current quality assessment methodology and the need of AI based quality assessment technology in India.
Current grain quality assessment methodology:
Value chain of agriculture in India, among many other things, focuses on the quality of produce. Market rates for them are also largely dependent on the quality. There are mainly two methods prevalent for grain quality assessment in India which are manual assessment and laboratory tests.
Manual assessment is time consuming and subjective as it depends upon the quality assayer assessing the produce. Laboratory tests are also time consuming and proves to be costly as prices are charged for each sample tested.
Need for AI based grain quality assessment:
Considering all of the above, one could say that there is scope for improvement. Surely, one can argue that years of experience in sourcing and distribution of grains may have trained the eye of the assessor to correctly assess the quality. That is a valid point, barring a few limitations, which are as follows:
- Lack of uniformity: Though buyers may be able to estimate the quality of grains, fact remains that it is merely an estimate. This estimate can be near enough to the actual quality, but it may also differ from person to person. This assessment is very subjective as one buyer may assess quality of grains to be better or worse than another buyer. Hence, the result is that for the same sample of grains, quality assessment varies. This leads to a need for a uniform quality assessment that everyone can agree upon.
- Issue for farmers: It is no secret that farmers usually don’t have much of a bargaining power and end up getting a lower value than they deserve. In addition, farmers are forced to sell at whatever price they get as they have already spent a lot out of their pockets for transportation. Fear due to lack of knowledge also plays a part in this. To combat this, APMCs and government procurement centres have been set up to reduce farmers’ exploitation. But even at these centres, price is dependent on quality and as discussed, quality is dependent on the assessor. In such a case, buyers may form a cartel to procure grains at a lower price, claiming the quality of grains to be lower than the actual quality.
- Time consuming: To comply with government regulations, exporters may have to get their grains tested in laboratories. This is incredibly time consuming as it take 2-3 days and the cost also adds up every time a sample is tested. In case of manual assessment, measuring size of grains through vernier caliper, requires considerable amount of time and calculations.
- Food loss: Inaccurate grain quality assessment can lead to huge amounts of loss, especially during storage. Moisture content is an important parameter during storage, as its excess can lead to growth of mold, fungi and bacteria. This growth leads to an increase in heat which furthers the development of mold, fungi and bacteria. Hence, it becomes necessary to check the quality of grains at frequent intervals. If this check is done inaccurately, it can lead to spoilage and loss of grains.
AI based grain quality assessment:
AI can be a game changer in quality assessment techniques as it has now become possible to overcome all the above challenges through the use of AI.
AI based grain analysers are now available in the market which are able to assess the quality of grains within seconds and give an accurate report of their quality. This technology requires to scan a sample of grains. Based on the scan, the technology is able to assess quality through advanced machine learning and computer vision.
With this technology, the time consuming and subjective process of manual quality assessment can be replaced to introduce a quick, reliable and accurate quality assessment.
An example is Upjao Easy, which can scan the grains and give a report on their quality in 45 seconds. The quality parameters taken into consideration are moisture content, weevilled kernels, fungus infected kernels, no. of broken kernels and much more.
With such a technology:
- traceability, during the entire process of grain quality assessment, is maintained
- uniformity regarding the grain quality is maintained:,
- farmers, and organizations working for the welfare of farmers, can provide the quality report as proof to avoid exploitation,
- digital transparency regarding grain quality is maintained
- the assessment is quick and accurate, thus reducing the time required for assessment and helping in making informed decisions.
Hence, AI can become a significant contributor to ensure quality across the entire agricultural value chain. It is now up to the people on how this technology is integrated in the current process.