Why real-time customer data and actionable insights are the need of the hour

The world population is experiencing a rapid boom, and it is predicted that it will approach a staggering 9.7 billion individuals by the year 2050. As a result, this rising growth has created a growing fear about the ability to satisfy the increasing demand for food, all while ensuring the important aspects of food security and sustainability remain intact.

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

Artificial intelligence (AI) embodies the ability of machines or computer programs to undertake endeavors typically dependent on human intellect, which include 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 include machine learning (ML), deep learning, natural language processing, computer vision, robotics, and cognitive computing. Within AI technology, a plethora of algorithms are emerging, including reinforcement learning, swarm intelligence, cognitive science, expert systems, fuzzy logic (FL), Artificial Neural Networks (ANN) and logic programming, providing a rich tapestry of tools to use in pursuit. of intelligent automation.

Innovative applications of AI in food and agriculture

GRAIN QUALITY

Manual grain inspection is a time-consuming process and is prone to human error, which can lead to the selection of lower quality grains. 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 images of grains and detect defects or impurities, such as broken kernels, foreign materials, etc.

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

PEST DETECTION AND WEED MANAGEMENT

Accurate identification of insect species, size variation and developmental stage is crucial for effective pest management in agriculture. By identifying the type and number of insects present in a cropland, farmers can take appropriate measures to control the pest population and prevent damage to their crops. Various 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 reduce the negative impact 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 favorable than cutting-action weeding devices, as opposed to time-based weeding, it is possible to remotely regulate the inclination of tines of spring tine harrow prototype 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, FL, USA), are designed to automate the harvesting of strawberries, which is a labor-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 reduce labor costs and improve their yields by ensuring that more strawberries are harvested at the optimal time.

FOOD SAFETY COMPLIANCE

AI-enabled cameras are used to ensure safety compliance among food workers in food facilities. It uses facial recognition and object recognition software to determine whether workers observe good personal hygiene as required by food safety legislation. If violation is found, it extracts the screenshots for review which can be corrected in real time. The accuracy of this technology is more than 96%.

PRODUCT DEVELOPMENT

AI technology uses machine learning and predictive algorithms to model consumer flavor 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. This allows manufacturers to know which products will thrive before they hit the shelves.

Companies like SPOONSHOT use AI techniques like NLP (natural language processing) and computer vision to build organized information from unstructured data. They use food science domain knowledge to process data related to physical and chemical properties of ingredients to understand how ingredient interactions affect a final recipe.

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

MARKET RESEARCH AND SALES CONNECTION

AI offers tremendous potential to assist with market research within 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 businesses adapt their products, marketing strategies and overall consumer experience 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, competitor analysis, etc., and help make smarter, data-driven decisions. (11)

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

Companies like THE LIGHTBULB.AI use AI-enabled technology to offer a range of research services. This includes qualitative and quantitative research, ad testing, as well as UI/UX testing. Their advanced capabilities include face 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 leveraging AI in research, businesses can overcome the shortcomings of traditional methods and gain a deeper understanding of consumer behavior. AI-based research offers the benefit of speed, accuracy and scalability, enabling companies to quickly adapt 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 improving overall sales efficiency.

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

CLOSURE

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 improved productivity, sustainability and innovation.

Through advanced algorithms and data-driven insights, AI optimizes crop management, improves yield predictions and revolutionizes farming practices. This enables precise monitoring of soil conditions, crop health and irrigation needs, leading to resource-efficient and environmentally conscious agricultural operations.

Furthermore, AI improves food safety by rapidly detecting and mitigating risks related to contaminants, pests and diseases. It facilitates traceability and supply chain transparency, and ensures 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 nutritional recommendations. It drives the development of new ingredients and flavors and expands 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 utilize the potential of AI while ensuring equitable access and sustainable practices.

In this era of grabs and bites, AI holds the promise of a transformative future for food and agriculture, ushering in a new era of abundance, efficiency and global nutrition. Let’s seize the opportunities 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 of experience on innovation, technology, quality and 6 years in clinical pharmacokinetics.