Subrat Panda is the Chief Technology Officer at AgNext Technologies, a leading agritech start-up that provides deep-tech solutions to enable AI-led food trade across all value chains.
Agriculture, in recent times, has radically moved towards significant digital transformations. From advancement in machines to deep-tech interventions, the evolving sector is notably creating room for frontier technologies like artificial intelligence (AI), IoT, and data analytics across food value chains.
In contrast to the traditional techniques of manual intervention, artificial intelligence has made the food trading process more transparent, quick, and efficient. This AI-driven approach intends to cut down the processing time to a matter of seconds, thereby making the process simpler, quicker, and standardized.
Traditional processing methods of the food value chain, with their unstandardized approach and sensory perceptions, are highly subjective in nature and are capable of leaving huge room for errors. A better alternative to manual intervention is the tech-driven computerized sensors that inject accuracy to the system, thereby making the process more standardized. Not just this but the integration of accuracy to the working process fosters a healthy relationship of trust and credibility throughout the system.
With the trifecta of AI, IoT, and Data Analytics, today we have the capability to reduce the complexities associated with traditional operational processes that hamper productivity and profitability in the food value chain.
The application of artificial intelligence to solve agricultural problems not only has the potential to solve the long-standing legacy issues, but also create a plethora of opportunities across the ecosystem. However, the monumental task that lies ahead of us is to train the AI with enough data-sets to significantly improve accuracy results.
The story starts with integration of highly sensitive IoT sensors to capture the required data, which can then be categorised into actionable data-sets using data analytics models. These data-sets are then used for training the AI to independently recognize patterns over time. Consider these sensory devices as akin to human senses, to gauge patterns of information (data) which are sent to the brain for processing and decision making. Quite similar to a human child, a young AI model also needs to be taught to recognize information and guided to process it, so that it can develop patterns of behaviour as per direction.
It is the perfect integration of these frontier technologies which creates viable models that can be scaled as required. The more sophisticated the model and data fed into it, the better the output and consequent outcomes.
Unleashing the power of AI to enhance food quality
Quality in the food value chain is crucial to get the right value of the product, build consumer trust, and above all to feed the world with highly nutritious food. For instance, at present, the food quality assessment is a subjective process which is done manually, completely reliant on intuitive sensory methods or highly time consuming delays to generate nutritional profiles. The utilization of AI, IoT and data analytics simplifies the process of quality estimation and also makes it quick and standardized. Together, it allows a rapid transition from art to science, from abstract to precision, from human biases to machine-driven mechanisms that weed out errors and provide consistent data on a regular basis.
With that being said, a relevant question might crop up concerning whether artificial intelligence can truly replace existing legacy systems and improve efficiencies across food value chains. It is a relevant concern, agriculture systems are primarily based on traditional approaches and the implementation of these frontier technologies is still an evolving concept.
However, in the past few years, we have witnessed the exponential growth experienced by agribusinesses, post implementation of AI-led deep tech solutions. In a short span of time, utilization of frontier technologies not only helped agribusinesses to improve their profit margins, but also brought significant transparency and trust among buyers and sellers. Use of digital technologies also removed prior complexities from the quality assessment process, and made it faster, efficient and more accessible.
Challenges and opportunities
The data generated across the spectrum of agriculture is phenomenally diverse. Creating flexible AI-models that can adapt to diverse geographies, commodities and value chains is a huge undertaking. For instance, the model that works best for milk assessment cannot be implemented for grain assessment. This means that every food value chain demands unique interventions to reflect the food diversity. This is an incredible opportunity for deep-tech experts to create the blueprints of innovation in the agriculture sector, which will lay the foundation for the evolution of the sector into a tech-driven powerhouse.
Another challenge is lack of standardization in tech adoptions in the agriculture sector. Since agriculture systems are incredibly diverse and steeped in traditional operational methodologies, most of the technological adaptations have happened in silos and not in an integrated manner. Hence, building onto and scaling existing systems at a larger scale becomes a herculean task.
Despite the recent boom in technology-led interventions and improvements in the agriculture sector, it remains one of the most tech agnostic areas in the modern world. So the challenge is not just innovations, but also creating awareness about the value that deep-tech can bring for all stakeholders in the agri-ecosystems.
The expected transition from traditional to technological processes has once again set the agricultural sector on the verge of revolution and we, as the change-makers, are on the frontlines of the disruption. Undoubtedly, it is an exciting time to be a technologist working in the emerging agritech sector and being part of the transformational growth story of the conventional systems by assimilating the trifecta of AI, IoT and data analytics for agriculture 4.0.