Agroindustry 4.0
Thoughts from techno-economics revolution context in agricultural industry
Industries 4.0 transforming agri-food businesses
With the advancement of digitalization and technology, vertically and horizontally in the productive matrix of global economies, new ways of innovating began to be designed based on the transversal application of knowledge and technology; this marks a disruptive change on a new techno-economic paradigm, and what is currently known as the Fourth Industrial Revolution rises. Information technologies have come to play a fundamental role in the new framework of internal and external competitiveness, and knowledge rises as the most productive input of production today.
Within this context, it is unavoidable that the different sectors -in this specific case, the agro-industrial sector- allocate their efforts and resources towards the acquisition, adaptation and innovation in technologies, digital transformation and total knowledge management.
With the disruption of the fourth industrial revolution, led by technologies such as IoT (Internet of Things), artificial intelligence, blockchain, energy storage systems, biotechnology, drones or 3D printing, among others; both agribusiness and agroindustry as a whole have the opportunity and, we shall say, the obligation to carry out this technological catch-up in order to generate efficiencies in both processes and organizations (higher yields, lower costs and less environmental impact), and thus achieve competitive advantages in a market that is becoming more globalized and interconnected every day. This industrial and productive interconnectivity at a global level is what is called as Global Value Chains.
Additionally, the incorporation of these new technologies of Agroindustry 4.0 that add efficiencies to the different processes of the agro-industrial value chain aims to reduce the pressure that is currently exerted on the exploitation of resources. Thus, this also leads to a change in the management of these natural resources, innovations with a positive environmental impact, which allow reducing the carbon footprint.
To give an example of the virtues of this new paradigm and of these technologies applied to the value chain of the livestock industry, a strategic partnership was formed between Cargill and an Irish company called Cainthus. Together they are developing a system with artificial intelligence and bovine facial recognition technology. Smart cameras are placed in the different sheds and fields that identify each of the cows in a herd in seconds by their facial characteristics. Then, connected to software with artificial intelligence (specifically, deep learning), the system determines if the cow is eating well. It even detects if she is sick and can alert the livestock producer with an app on the phone. You can also observe the behavior of the entire herd as a whole to decide the best way to distribute the feed, or schedule the shifts of the cows in a pen or sector of the field. Over time, the platform learns from what it sees and records (this is the very process of machine learning) and begins to automate and make more efficient the daily care of each animal.
However, and more specifically, the different links of the agro-industrial value chain have , to a greater or lesser extent and increasingly, agro-intelligent processes. Starting with the seed treatment, IoT devices and decision models based on machine learning are currently used in order to maximize germination, optimize the use and consumption of resources and inputs in germination. On the agricultural production side, the advantages of artificial intelligence are also used here, where the development of the crop can be modeled algorithmically, in order to be able to plan its subsequent commercialization, estimate production, calculate suitable collection/harvest times, automate and make efficient irrigation processes, interact with different structural elements of the fields (such as greenhouses, climate control mechanisms, opening/closing of windows, etc.), and subsequently apply machine learning, deep learning or big data methodologies in general to help in decision-making processes. Finally, towards the end of the value chain with regard to marketing and logistics, and export; It is in this part where industrial processes have traditionally been managed, promoting with this industry 4.0 paradigm the interconnection with all the elements and automating all the processes to optimize resources, all connected with an system recording and advanced analysis of the information generated, applying disruptive technologies such as blockchain, which is a technology highly applied to logistics processes and that still has a lot of potential to develop in this area.
Finally, it should be noted that a priori it does not seem to be enough to just incorporate a new technology into an old industry or business scheme, these technologies have come to generate disruptive changes in the models, and it would seem that what is needed is a little more of a Schumpeterian dynamic: a creative destruction; that is, it is necessary to deprecate the old models to build new schemes based on the new ways of combining knowledge, which lead to new ways of innovations. For example, some companies that produce pesticides and fertilizers are using these technologies to offer better products and get them to market faster. But, in contrast, precision agriculture –which uses IoT, 3D aerial images generated from drones and AI to analyze the soil and the behavior of crops– is on track to reduce the need for fertilizers and pesticides. In other words, a better scheme is to discover and develop these new business models, instead of looking for better quality products; the focus is on finding better solutions for customer problems, whether they are farmers, suppliers or end consumers; that is, from end-to-end of each value chain.
