Smart farming through data analytics

Agriculture has had an important role in the development of society. By ensuring food security it permitted people of a society to pursue other gainful preoccupations to contribute to its progress. Thus, the earliest successful societies benefited from and initiated revolutions in agricultural practices that increased productivity in food production. Most recently, the last century has seen such a dramatic increase in food production that we live in an unprecedented time of excess.

After a century of such largesse, questions are being raised at the costs involved. What have we overlooked in achieving the productivity increase in agriculture? At the same time, it’s also a fact that there are still people who haven’t enjoyed access to this excess. And so we have contrasting problems of plenty and lack of food security. In other words, there is still a need to address inefficiencies in food production to cater to a growing population while keeping in check some of the counter-productive measures adopted in agriculture. With increasing awareness of nutrition, agricultural practices and cause-effects in the agri-environment there is a greater push towards a wholesome understanding of practices. Thus it’s no longer enough to just produce the biggest crop but the emphasis is on increasing qualitative yield with minimal detrimental effects to the environment affected by agriculture.

Smart farming

Just as rapid advances in mechanization helped usher in the previous agri revolution in the late 19th century, the tremendous advances in digital technology is ushering in the latest revolution of smart farming. This is the age of data and data is being leveraged to bring about this revolution. Indeed, computational methods, digital management systems and advanced sensors have made great advances in data gathering and analysis. Sources of data in the agricultural domain are mostly remote sensing technologies and farm equipment sensors. The increasingly dense space assets that provide multimodal remote sensing technologies, coupled with the advent of unmanned aerial and ground vehicles, is providing a capacity to survey large tracts of land across years and seasons. Besides these, it’s becoming increasingly common to find farming equipment with devices that sense various parameters such as total yield, geo-location, fertilization application rate and irrigation patterns.

All the devices and instruments provide diverse data types that can be effectively used to create multi-dimensional models to illuminate in greater detail the cause-effect relationships in agricultural practices. With the advent of the age of big data there are evermore opportunities in this domain. The question then is: how can we use data to improve the ways farming is done in a way that is both profitable and also sustainable in the long-term?

Farm Analytics at UCPori

At the Data Analytics and Optimization group (DAO) at the Tampere University (Pori Unit) we are doing precisely this: we are using our experience in data mining to understand proces

How can we use data to improve the ways farming is done in a way that is both profitable and also sustainable in the long-term?

ses and predict outcomes. We’ve identified needs among the local farmers in the Satakunta region whereby data can help with their farming operations. While methods to collect, analyse and present data have abounded,

its uptake by the farmer i.e. the stakeholder in our case, isn’t always rapid or uniform. Our aim is to establish a local connect to the research being conducted at the university.

MikäData, an European Innovation Partnership (EIP) funded project (2017-2019) was specifically tasked with providing an online data management tool where the farmers can upload and visualize their data. In addition to data storage and viewing, this system will be able to provide online tools to analyse the data. Ranging from the simple to the complex, these tools will help farmers to upload their own proprietary data, browse them along with relevant open data (satellite images, National Land Survey maps, etc.), and calculate performance measures. An example of a functionality we are working on is the capacity to predict the crop yield of a particular field through Artificial Intelligence models using aerial imag

es, which help the farmer monitor the progress of the crop and make necessary remedial actions.

During the course of the MikäData project, we have learnt valuable lessons in farming decisions that the farmers have to make and have a sense of the kind of data that would help them further. An essential lesson has been the importance they give to the condition of the soil. This helped us appreciate the vital role that soil plays in their operations. Thus the soil is seen not merely as a substrate to produce maximal crops, but as a complex mechano-bio-chemical medium that needs proper management to ensure healthy yields and long term stability.

The Way Forward

We have consequently started two projects – PeltoAI and Bioeväät – with Pyhäjärvi-Instituutti to study mechanisms for soil health management and improvement strategies in the Satakunta region. During these projects we will use data to help the farmers detect and diagnose problems on their fields, as well as monitor and evaluate the effect of their field improvement activities. We will help them take steps towards more data informed decisions and incorporate smart farming methods.

Data analytics in agriculture is a busy research topic at the moment, and we at DAO are excited to be part of it. There is an immense amount of research work being conducted over the world just waiting for us to tap into and help the local farmer to integrate into their daily practice.

TARMO LIPPING Professor Tampere University, Pori Unit

NATHANIEL NARRA Postdoctoral Research Fellow Tampere University, Pori Unit

PETRI LINNA Project Manager Tampere University, Pori Unit