How OgmentO is leading the way in retail automation

The world’s population is experiencing rapid growth and is expected to reach a staggering 9.7 billion people by 2050. As a result, this increasing growth has raised growing concerns about the ability to meet the growing demand for food, all while ensuring that important 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 increased sustainability.

Artificial intelligence (AI) embodies the ability of machines or computer programs to perform actions that are typically dependent on human intellect and include domains such as learning, reasoning, problem solving, and decision-making. The realm of artificial intelligence encompasses a variety of subfields, each bringing unique capabilities to this expansive discipline.

These subfields include machine learning (ML), deep learning, natural language processing, computer vision, robotics, and cognitive computing. A plethora of algorithms are emerging in AI technology, including reinforcement learning, swarm intelligence, cognitive science, expert systems, fuzzy logic (FL), artificial neural networks (ANN), and logic programming, offering a rich set of tools to use in the pursuit of purpose. intelligent automation.

Innovative applications of artificial intelligence in food and agriculture

GRAIN QUALITY

Manual grain inspection is a time-consuming process and is prone to human errors, which may result in the selection of lower quality grain. Therefore, the use of computer vision systems in grain inspection is becoming more and more popular. These systems use advanced imaging techniques and ML algorithms to analyze grain images and identify defects or contaminants such as broken grains, foreign bodies, etc.

Back-propagation neural network (BPNN) was successfully used to classify rice grain varieties with high accuracy (96%), even with poor image quality.

PEST DETECTION AND WEED CONTROL

Accurate identification of insect species, size and development stage is crucial for effective pest control in agriculture. By identifying the type and number of insects present in a crop field, farmers can take appropriate measures to control pest populations and prevent crop damage. 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, overuse or misuse of herbicides can have negative impacts on both human health and the environment. To minimize the negative effects of herbicides, there is a growing need for more precise and accurate application methods.

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

While intelligent mechanical weed control would be more effective than weed control devices with a cutting function, as opposed to time-based weed removal, it is possible to remotely adjust the tine tendency of prototype spring harrow systems based on soil conditions, weed density and crop production.

CROP SELECTION AND YIELD IMPROVEMENT

Robots such as the Berry 5 Robot from Harvest Croo Robotics (Tampa, Florida, USA) are designed to automate strawberry picking, which is a labor-intensive and time-consuming process.

The robot uses image recognition and ML algorithms to identify and pick ripe strawberries faster than a human can. This can help farmers reduce labor costs and improve yields, ensuring more strawberries are harvested at the optimal time.

FOOD SAFETY COMPLIANCE

AI-enabled cameras are used to ensure safety compliance among food workers in a food plant. It uses facial and object recognition software to determine whether employees are following the personal hygiene practices required by food safety regulations. If a violation is detected, it retrieves screen images for review and can be corrected in real time. The accuracy of this technology is over 96%.

PRODUCT DEVELOPMENT

Artificial intelligence technology uses machine learning and predictive algorithms to model consumers’ taste preferences and predict how well they will respond to new tastes. Data can be broken down into demographic groups to help companies develop new products that meet the preferences of their target audience. They would allow manufacturers to know which products will do well before they hit the shelves.

Companies like SPOONSHOT use artificial intelligence techniques such as NLP (natural language processing) and computer vision to create structured information from unstructured data. They use food science knowledge to process data related to the physical and chemical properties of ingredients to understand how interactions between ingredients affect the final recipe.

SPOONSHOT can browse 3B social conversations, 5M research articles, 84M articles, 4M products, 84M blogs, etc. to provide useful information on product concepts, product and menu innovations, consumer market knowledge, competitive analysis, etc.

MARKET RESEARCH AND SALES ENABLING

Artificial intelligence offers enormous potential to aid market research in the food industry, providing valuable insights and facilitating better decision-making. AI algorithms can analyze vast amounts of data, including consumer preferences, purchasing behavior and social media interactions related to food.

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

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

In today’s attention economy, where gaining and maintaining attention is challenging due to the overwhelming choices and distractions consumers face, traditional market research methods have their limitations. However, AI-based research offers a promising solution, providing fast, 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, advertising tests, and UI/UX tests. Their advanced capabilities include 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 using artificial intelligence in research, companies can overcome the shortcomings of traditional methods and gain a deeper understanding of consumer behavior. AI-powered research offers the advantages of speed, accuracy and scalability, enabling companies to quickly adapt to changing market dynamics and make informed decisions based on solid data-driven insights.

Artificial intelligence can significantly contribute to increasing sales by providing valuable information, automating tasks and increasing overall sales efficiency.

INFILECT, a leading provider of advanced retail visual intelligence, offers cutting-edge solutions that can significantly increase sales for organizations. With advanced image recognition technology and retail data analytics capabilities, Infilect enables companies to improve on-shelf visibility and increase store fulfillment efficiency. By analyzing visual data such as product placement, inventory availability and planogram compliance, Infilect provides valuable information to optimize sales strategies and improve overall retail performance.

APPLICATION

In summary, the transformative power of AI in the food and agriculture industry is undeniable. From the bytes of data to the very pieces we consume, AI has become a driving force for increased productivity, sustainability and innovation.

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

Additionally, artificial intelligence improves food safety by quickly detecting and mitigating threats related to contaminants, pests and diseases. It facilitates traceability and transparency in the supply chain, ensuring consumers have access to safe, high-quality food.

Beyond the farm, artificial intelligence is revolutionizing food production, from automatic processing and packaging to personalized nutritional recommendations. It drives the development of new ingredients and flavors, expanding the boundaries of culinary creativity.

However, it must be admitted that the implementation of artificial intelligence in the food and agricultural sector is a continuous process.

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

In the age of bytes and bites, artificial intelligence promises a transformative future for food and agriculture, ushering in a new era of abundance, efficiency and global nourishment. Let us seize the opportunities before us and use AI as a powerful ally in creating a more sustainable, resilient and inclusive food system for future generations.

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