Surpassing competition: How Super Apps Can Deliver Great CX with Observability

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The world’s population is experiencing a rapid surge, and it is foreseen to approach a staggering 9.7 billion individuals by the year 2050. Consequently, this escalating growth has engendered a mounting apprehension regarding the ability to satisfy the escalating demand for sustenance, all while ensuring the vital aspects of food security and sustainability remain intact.

In light of these concerns, the integration of artificial intelligence (AI) applications in the agri-food sector holds extraordinary potential to revolutionize the industry, heralding a new era of heightened sustainability. 

Artificial intelligence (AI) embodies the capacity of machines or computer programs to undertake endeavours typically reliant on human intellect, encompassing domains such as learning, reasoning, problem-solving, and decision-making. The realm of AI encompasses diverse subfields, each contributing unique capabilities to this expansive discipline. 

These subfields encompass machine learning (ML), deep learning, natural language processing, computer vision, robotics, and cognitive computing. Within AI technology, a plethora of algorithms emerges, including reinforcement learning, swarm intelligence, cognitive science, expert systems, fuzzy logic (FL), Artificial Neural Networks (ANN), and Logic Programming, offering a rich tapestry of tools to leverage in pursuit of intelligent automation.

Innovative Applications of AI in Food and Agriculture


Manual grain inspection is a time-consuming process and is prone to human error, which can result in the selection of lower-quality grains. Therefore, the use of computer vision systems in grain inspection is becoming increasingly popular. These systems use advanced imaging techniques and ML algorithms to analyse images of grains and identify defects or impurities, such as broken kernels, foreign materials etc. 

Back propagation neural network (BPNN) has been effectively used to classify rice grain varieties with great accuracy (96%), even with poor image quality. 


Accurate identification of insect species, size variation, and stage of development is crucial for effective pest management in agriculture. By identifying the type and number of insects present in a crop field, farmers can take appropriate measures to control the pest population and prevent damage to their crops. Several AI and ML technologies are being developed and tested for insect detection and counting. 

Some of these technologies use computer vision algorithms, while others rely on ML algorithms to identify and classify different insect species. 

Similarly, herbicides have been widely used by farmers for many years to control weeds and improve crop yields. However, the overuse or improper application of herbicides can have negative impacts on both human health and the environment. To minimize the negative impacts of herbicides, there is a growing need for more precise and accurate application methods.

Robotic weed control is also an emerging technology that shows great promise for the future of agriculture. Robotic weed control systems typically use computer vision and ML algorithms to detect and identify weeds in crop fields, then use robotic arms or other mechanical tools to remove or destroy the weeds. 

Although intelligent mechanical weed control would be more felicitous than weeding devices with cutting action, contrary to time-based weed removal, it is possible to remotely regulate the tendency of tines of spring-tine harrow prototype systems based on the conditions of soil, the density of weed, and crop production. 


Robots, such as the Berry 5 Robot from Harvest Croo Robotics (Tampa, FL, USA), are designed to automate the harvesting of strawberries, which is a labour-intensive and time-consuming process.

The robot uses computer vision and ML algorithms to identify and pick ripe strawberries at a faster rate than humans can. This can help farmers to reduce labour costs and improve their yields by ensuring that more strawberries are harvested at the optimal time. 


AI enabled cameras are used to ensure safety compliance amongst food workers in food facility. This employs facial-recognition and object-recognition software to determine whether workers are complying with good personal hygiene as required by food safety law. If violation is found, it extracts the screen images for review which can be rectified in the real time. The accuracy of this technology is more than 96%. 


AI technology uses machine learning and predictive algorithms to model consumer flavour preferences and predict how well they will respond to new tastes. The data can be segmented into demographic groups to help companies develop new products that match the preferences of their target audience. With these, manufacturers could know what products will thrive before the hit the shelves. 

Companies like SPOONSHOT are using AI techniques like NLP (natural language processing) and computer vision to build organised information from unstructured data. They leverage food science domain knowledge to process data relating to physical and chemical properties of ingredients to understand how ingredient interactions impact a final recipe.

SPOONSHOT can scout 3B social conversations, 5M research papers, 84M articles, 4M products, 84M blogs etc to provide actionable insights around product concepts, product and menu innovations, consumer market insights, competitor analysis etc. 


AI offers tremendous potential to assist in market research within the food industry, providing valuable insights and facilitating better decision-making. AI algorithms can analyse vast amounts of data, including consumer preferences, purchasing behaviour, and social media interactions related to food. 

By recognizing patterns and correlations, AI can identify emerging trends, understand consumer preferences, and predict future demands accurately. This information can help food businesses tailor their products, marketing strategies, and overall consumer experience to meet evolving customer needs.

Social listening tools like CRIMSON HEXAGON and SYNTHESIO helps generate valuable insights around audience analysis, brand intelligence, campaign analysis, customer sentiment, market research, trend analysis, competitor analysis etc, helping make smarter, data-driven decisions. (11)

In the current attention-driven economy, where capturing and retaining attention is challenging due to the overwhelming choices and distractions faced by consumers, traditional market research methods have their limitations. However, AI-powered research offers a promising solution by providing rapid, reliable, and actionable insights.

Companies like THE LIGHTBULB.AI leverage AI-enabled technology to offer a range of research services. These include qualitative and quantitative research, ad testing, as well as UI/UX testing. Their advanced capabilities encompass facial coding, eye tracking, speech transcription, text sentiment analysis, and audio tonality analysis. These modules enable comprehensive analysis and understanding of user experiences and preferences. 

By harnessing AI in research, businesses can overcome the shortcomings of traditional methods and gain a deeper understanding of consumer behaviour. AI-based research offers the advantage of speed, accuracy, and scalability, allowing companies to adapt swiftly to evolving market dynamics and make informed decisions based on robust data-driven insights.

AI can significantly contribute to sales enablement by providing valuable insights, automating tasks, and enhancing overall sales efficiency. 

INFILECT, a leading provider of advanced retail visual intelligence, offers cutting-edge solutions that can greatly boost sales for organizations. With their advanced image recognition technology and retail data analytics capabilities, Infilect empowers businesses to improve shelf visibility and enhance store execution performance. By analysing visual data, such as product placement, stock availability, and planogram compliance, Infilect provides valuable insights to optimize sales strategies and improve overall retail performance. 


In conclusion, the transformational power of AI in the food and agriculture industry is undeniable. From bytes of data to the very bites we consume, AI has become a driving force behind enhanced productivity, sustainability, and innovation.

Through advanced algorithms and data-driven insights, AI is optimizing crop management, improving yield predictions, and revolutionizing farming practices. It is enabling precise monitoring of soil conditions, crop health, and irrigation needs, leading to resource-efficient and environmentally conscious agricultural operations.

Furthermore, AI is enhancing food safety by rapidly detecting and mitigating risks associated with contaminants, pests, and diseases. It is facilitating traceability and supply chain transparency, ensuring that consumers have access to safe and high-quality food.

Beyond the farm, AI is revolutionizing food production, from automated processing and packaging to personalized nutrition recommendations. It is driving the development of novel ingredients and flavors, expanding the boundaries of culinary creativity.

However, it is essential to recognize that the adoption of AI in the food and agriculture sector is an ongoing journey. 

Challenges such as data privacy, infrastructure limitations, and ethical considerations must be addressed to fully harness the potential of AI while ensuring equitable access and sustainable practices.

In this age of bytes and bites, AI holds the promise of a transformative future for food and agriculture, ushering in a new era of abundance, efficiency, and global nourishment. Let us seize the opportunities that lie ahead and embrace AI as a powerful ally in creating a more sustainable, resilient, and inclusive food system for generations to come.


Bharat Sawnani is the founder of Elevantus with 14 years’ experience across innovation, technology, quality, and 6 years in clinical pharmacokinetics.  


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