Introduction As reported on October 9, 2020, some startups have come up with the idea of using artificial intelligence (AI) for on the spot quality assessment of different farm produces. This innovative technological intervention will help farmers to determine the quality of their produce and accordingly they can demand the price, in and outside agricultural produce markets (APMCs).
Quality Counts In a real sense, in determining the price of a product, quality is the correct yardstick, not quantity. In case of farm produce, a major concern is to measure quality, taking into consideration the large volume of the produces. It was impossible to test and check each grain. However, the ‘Amul model’ provided the real clue as to how to pay the farmers not just for the quantity of their produce but also for the quality. Amul used technologies like the Gerber centrifuge and lactometer that helped farmers to get more for the same volume of milk, containing higher total solids, e.g., pure and good. Amul’s novel technology provided clue to determine quality and served as a base for democratic pricing——best money for superior quality.
This was not the case for other farmers involved in crops, which are sold through APMC (agricultural produce market committee) mandis, where some quality premium was given based on subjective assessment. Here quality assessment followed unscientific physical test criteria.
In this sort of system, the farmer proved to be a loser as he could not bargain with traders. There’s no equivalent of a Gerber-lactometer reading that he can cite to demand a better price, even if his crop is above fair average quality. But can produce quality be assessed without depending on the eyes, nose, and tongue of people? And can it be done at the mandi – that too, instantly?
The answer to thin question is application of AI for quality check of farm produce. New artificial intelligence (AI) technologies – computer vision and spectrometry – hold in this regard promise a lot of AI is about seeing like a computer, but analysing like a human being, faithful and dependable. Just as the Tesla self-driving car is trained to recognise red lights and roads, and know when to stop and when to move, we can replace humans with machines to do both physical and chemical analysis of food. Such analysis would be instant, on the spot and without going to a lab.
An Indian Institute of Technology (IIT) Kharagpur-incubated Company Ag next technologies, has developed machine-learning algorithms that can do quality assessment of various farm produce in 30 seconds, including physical analysis through computer vision. It has a handheld device SpecX Visio that one can take to the mandi and click a picture of a sample of, say, rice. It will digitise that image and immediately tell how many of the grains are broken, shriveled, weevilled (bored by insects) or chalky (opaque), thus determining the quality. AgNext has also come out with an AI-based solution to undertake fine leaf count in tea. Tea that is plucked could be one bud one leaf, one bud two leaves, one bud three leaves or even banjhi (no bud) with the AI one can accurately count as well as classify leaves, buds, banjhi and shoots in any sample through image recognition.
AgNext’s algorithm models based on spectrometry can analyse the chemical characteristics of food. It draws on the concept that when light is thrown at different wavelengths on an object, that object (produce) would generate a reflectance spectrum graph. Each of its molecules will, in turn, have a unique signature in that graph. The molecular signature of the chemical of interest – it could be curcumin (the key active ingredient in turmeric), capsaicin (which imparts pungency to chilli), polyphenols in tea, caffeine in coffee, nicotine and chloride in tobacco, gluten in wheat and amylose in paddy – can then be analysed and an AI model developed after scanning several samples of the produce. It has prepared reflectance graphs for many commodities which are proved to be accurate determining different qualities for a same produce of different locations of India. AgNext’s SpecX series of computer vision and spectrometry algorithms-based devices – one of them can instantly analyse even protein, lactose and the presence of adulterants such as palm oil in milk – operate on a common AI ‘Qualix’ platform. Being lightweight, they can be taken to the field, with internet connection required only to transfer data onto the platform.
These are other startups like Intello Labs and Nebulaa Innovations Private Ltd, from IIT Bombay and Jodhpur, respectively which are working in the same line.
The Gurugram-headquartered company’s mobile app Intello Track uses neural network machine learning algorithms to analyse physical quality of fruits and vegetables. The app captures images of a representative sample of the produce that is brought and categorises it as grade A, B or C. Thus, grade A onions are defect-free and of 40-55 mm diameter, while grade B will have no skin with below 40 mm or above 55 mm size. In Grade C onions, there would be cuts or cracks with double patti (skin), sprouting, and smut infection.
The near infrared spectroscopy scanner of Intells Labs measures Brix/total soluble solids for sweetness, pH, dry matter, moisture and pesticide residues in apples, mango, papaya and sapota. The company has developed algorithms for 55 horticultural produce. Similarly, Nebulaa’s MATT Grain Analyser can give a full morphological analysis of cereals, pulses, mustard, soyabean and cumin – from their broken and admixtures content to length/breadth ratio and thousand count weight within one minute.
Way Forward The recent reforms that open up produce trading outside APMCs create conducive conditions so that the farmers get their due. However, the success of AI-based quality assessment depends on the creation of an ‘exhaustive library’ of images from millions of crop samples. Whatever images the handheld devices capture have to be compared and correlated with this library.
Most mandis have no systems to objectively grade produce based on colour, size, and visual defects. Quality assessment should become digital, verifiable, and scalable. Once produce is automatically graded and standardised, the power of assaying will get democratised and taken away from traders.
Courtesy: The Indian Express