Artificial Intelligence in Agri-food
Will the most advanced technologies revolutionise even the most distant fields?
Artificial intelligence dominates our headlines and has become a ubiquitous presence in our lives. The widespread adoption of this technology is not solely due to the availability of generative AI systems that have caused uproar and resonance, but also due to the proliferation and advancement of digital applications in various areas of work. The sectors involved are diverse, including those where technology has been automating and expediting operations for decades through the use of heavy machinery, as well as those where innovation is rare.
Recent market studies have shown that data-driven solutions are being introduced in various sectors to develop advanced applications for decision making, quality improvement, and production control. Additionally, the data demonstrates how advanced digitization systems are being implemented in industries traditionally perceived as distant from technology, such as agriculture and the food sector, revealing intriguing use cases.
AI in the food macro sector: leading application areas, from distribution to agricultural production.
The food sector, encompassing the entire supply chain, is diverse and complex. From agriculture, which is bound by the times and needs of nature (indeed considered a challenging sector to evolve with new technologies), to food manufacturing, which has become increasingly sophisticated with the use of advanced machinery, to the distribution sector, which has integrated digital management tools to ensure efficient service.
The rise of AI applications, especially in the Beverage field, is expected to grow by 45 percent due to solutions that include both new recipe development and supply chain management; this whole part of services dealing with storage, transportation and tracking of goods seems to be driving the entire industry.
Indeed many of Kode’s success stories in this area concern applications such as Kodelivery and its logistics services. Initially, the focus was on optimizing deliveries for both FTL and LTL, taking into account various constraints, primarily load maximization. Over time, however, companies have required tools to optimize other aspects, particularly in the area of warehouse management and storage, which are difficult to manage manually. We have therefore developed solutions such as PalletguardAI and Warehouse-as-a-city to digitize pallet handling. PalletguardAI provides detailed data of each good, including location, weight, and dimensions, in a single platform. Warehouse-as-a-city offers a digital twin of the warehouse, allowing visualization of traffic and arrangement of goods with automated optimization. Both solutions give insights and indications to improve pallet handling efficiency.
If for years there has been a great interest in AI-based solutions for goods management, the entry of Artificial Intelligence in the agricultural sector is proving to be astonishing and disruptive. Its evolution is linked not only to the automation of repetitive operations but also to the multivariate collection and analysis of large amounts of data from various sources. This includes the detection of weather patterns and verification of crop or soil quality. This enables farmers to make choices that increase productivity, reduce waste and environmental impact.
The value chain for this industry also involves manufacturing, where the development of Artificial Intelligence is closely tied to improvements in machine learning algorithms and data analytics. These technologies have reached a mature stage, allowing manufacturing companies to extract valuable information from large amounts of data in real-time. Manufacturers can use AI to predict production problems, optimize schedules, and reduce downtime, resulting in increased efficiency.
Although these areas of development have been extensively explored, it is noteworthy that several startups are now entering the market with advanced solutions specifically aimed at enhancing consumer satisfaction. These solutions include low-cost customization projects and, particularly in the food sector, product quality control and assurance measures.
New frontiers: real-time quality control
Quality control is a crucial aspect for any manufacturing company, regardless of the field, whether it be agriculture, food production, industrial chemicals, or highly technological products such as medical instruments. It has significant potential for improvement, both in terms of cost reduction and as an operational tool for optimizing production.
Currently, quality control is only applied to a limited number of samples at the end of production, in accordance with legal requirements. It is only capable of verifying compliance with standards and may result in the rejection of non-compliant products.
In the realm of Artificial Intelligence quality control, we cannot be satisfied with that. Our FactorAI framework, which incorporates over a decade of Kode’s experience in the manufacturing field, can predict, for example, waste production by identifying drifts. This enables plant operators to intervene immediately and correct any issue. Manufacturers can improve the quality of their production and reduce energy costs and raw material waste by following this approach.
But the future goes even further: the integration of FactorAI with IoT allows for real-time quality control. Different technologies, such as NIR spectroscopy developed by our partner Buchi, can be used to detect useful characteristics during in-line production. This enables us to determine the level of quality of the final product, whether it is cheese, gold bars, chemicals, or wine.
Indeed, the real-time quality control paradigm is also showing interesting developments in the agribusiness world, of which -thanks to our collaborations with the scientific and research world- we have already participated in experiments in precision agriculture. This is where AI brings unparalleled value and becomes key to productions that are not only better performing for the producer, but of greater relevance and benefit to the end consumer.
