Enabling smart farming with IoT technologies: The case of smart agriculture



How can a smart and connected agricultural industry exponentially increase its productivity through the internet of things


The accelerated expansion of global supply chains in the last 70 years, caused largely by the availability of better and faster means of transportation and more recently by the explosion of telecommunication means, have generated an abundance of food without precedent in the history of mankind. This has meant that agricultural products, previously considered autochthonous, essential or specific to a region or a country, have become common consumer goods anywhere in the world.


The availability and endless appetite of the markets for these agricultural products has generated both an opportunity for producers and pressure to be increasingly competitive and increase their productivity rates. This has led, in the second half of the last century, to an agricultural industrialization that is as strong as it is uneven. This, because while the regions, countries or business conglomerates with a larger productive scale managed to have access to means of planting, harvesting, transporting and mechanized processing; others who did not have access to these technologies and were left behind in that technical wave. Thus today we see marked differences in rudimentary farming methods between small farmers in less developed countries, and the highly equipped farmers in more industrialized countries.


On the other hand, towards the end of the 20th century and the first part of the 21st century, we have witnessed a new disruptive technological wave, led this time by infocommunications and whose axis of value is centered on the infinite availability of information of all kinds. This new technological wave is based on data and has been covering all areas of human activity. Of course the agricultural world has not been the exception.


The use of new digital technologies has become the new differentiator in the market. Thanks to sophisticated digital and electromechanical mechanisms, today we see the harvesting of tropical fruits in the Israeli desert and lettuce in closed buildings in Singapore, all under the infallible supervision of automated algorithms in top-tier computing infrastructure.


We are therefore at the gates of an unprecedented disruption that attempts to force the laggards in the first industrial wave to remain doubly folded before a change in the productive paradigm that will inevitably affect all agro-industrial activities.


Here are three big challenges facing the agricultural industry today and how IoT technologies can help solve them.



Productivity

Clearly the main objective of any agricultural process; at least of those that are not destined for self-consumption, is to be able to place products in the markets at prices that are convenient for the producer and generate enough income to maintain and increase their production. To achieve this, growers increasingly require better farming tools, seed varieties, and agricultural inputs.


But even with the best conditions, the great increases in productivity, measured through the efficiency in the use of resources, will depend in the future more and more on the availability and use of technology as a tool for decision-making.


This is where the combination of sensors connected to the internet, satellite images or those taken by drones, offer essential information to make processes such as planting, irrigation, fertilization or harvesting more efficient. Thus, for example, remote stations for measuring meteorological variables can indicate the best conditions for aerial spraying, humidity sensors can accurately indicate the water requirement in each of the individual plots and at different depths of the soil, to adapt to each type of cultivation and therefore better plan the use of water resources, which usually requires expensive investments in pumping or transportation.


Climate change

One of the great challenges of modern agriculture is planning its natural seasonality in a context of global warming that generates distortions in global, regional, local atmospheric phenomena and even at the level of microclimates in certain farms.

The difficulty of predicting climate variability and the intensity and duration of each event has made agriculture increasingly dependent on close monitoring of weather conditions. Furthermore, in some countries whose topographical diversity generates an extensive number of microclimates, individual measurement at the farm level, plots and even plots, is becoming a latent need.


The combination of data generated by IoT devices, both atmospheric and at ground level, with other historical variables of the agricultural process, varieties planted, inputs and types of soil can, through information processing technologies known as Machine Learning, allow the generation of of multivariable mathematical models and much more accurate forecasting algorithms that help improve good practices and redefine the most traditional agricultural recipes.


Differentiation, competence and responsibility

An element that cannot be ignored is that all productive activity today is framed to a greater or lesser degree in an environment of competition. An environment where the scrutiny of the market and the differentiation with elements of value for the client form a protective shield for those who manage to retain a captive market, or a barrier for those who fell further behind in the race.


From this point of view, we see how competition in the agricultural world has been pressuring companies and producers to incorporate certifications such as FairTrade, Global GAP, USDA Organic, Non GMO, Carbon Neutral, etc. Many of these hallmarks propose a series of guidelines for the agricultural process and its absolute traceability from field to table. At the same time, those who wear it are covered with an image of responsible companies that care for the environment, their workers, their community and their end customers with superior quality products.


With the use of technology, producers can not only support each other to more efficiently comply with the strict records and controls imposed by the certifying entities, but also go further and take advantage of the use of sensors and equipment on the farm to generate contact between the final consumer and his farm. Thus, for example, through a QR code on the product label, the consumer can access a traceability record of the product, where they have a history of the farm, supplies, working conditions, weather and even a photograph of the place. Close contact with that story will generate greater brand loyalty and a greater possibility of purchase and repurchase.


Final Conclusions

Companies can use sensors to measure meteorological elements and soil conditions to better plan their planting, irrigation, fertilization and felling tasks.


The combination of IoT information sources with other technologies such as multispectral images and historical farm data can generate predictive models and simulations that allow companies to improve their practices and recipes using cutting-edge technologies such as machine learning or computational learning.


All of this allows producers to both improve the internal efficiency of their processes, prepare for the effects of climate change, and also better compete in the markets.

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