Last Updated on: September 2, 2022
India has always been known as the land of agriculture, across the globe. Like how the country has a national animal, a national bird, and the likes, it also has a national profession as Agriculture. At least this was how it used to be, before all the technology and digital boom happened.
Farmers right now are at a crucial juncture in their professional trajectories. They could choose to follow thor traditional methods or could bend a little and take help from the vast technology around them.
Traditional methods are of course very good and a proven method for tremendous crop yield. However, the world today is moving at an ultra-speed. It is very important for a person, irrespective of the domain he is in, to catch up to it. Else, he/she would be lost in time, ideas and practices.
Being the backbone of the country’s economy, Agriculture gets affected every year due to a large number of factors, namely: natural disasters such as droughts, floods, fires, and man-made ones like land encroachment by corporates, tax increase, and the likes.
Though there are existing welfare schemes for handling the same, the efficiency and the effect of these schemes are quite low.
This is where technology and especially Data Science in agriculture could prove immensely helpful.
Technology has seeped into every part of our society. Though it has its drawbacks, it has helped to a great extent. Data Science has been an evolving area of study and research for quite some time now. This methodology could be used in agriculture to improve the overall process, from end-to-end.
This article covers how Data Science could be used to leverage Agronomy. Agronomy is simply the science and technology of cultivating and using plants in agriculture and related areas.
Table of Contents
Factors Driving Innovation in the Field of Agriculture
The agriculture sector is witnessing an increase in data-driven decisions that result in higher productivity while helping consumers have greater transparency about the food they consume. Advancements in technology have made it possible to collect data about soil, water, and minerals and use this to optimise farming and make smart decisions. The advent of IoT (Internet of Things) has made sharing of information easier and allowed farmers to study, analyse and process information for their benefit. Some factors that are driving innovation and the use of data science in agriculture are:
Need for Correct Predictions
A major factor driving innovation is that most farmers make agricultural decisions based on intuition, rather than scientific reasons. A wrong decision can prove to be costly, affection production through a complete season. Farmers need to find innovative ways to make correct predictions and plan their produce accordingly.
Collection of Correct and More Data
Technology can be used to gather more data that is appropriate for making farming-related decisions. The use of nanotechnology and various types of sensors can help gather more data for a better output.
Consumers Seeking More Information
A change in the behaviour of consumers towards knowledgeable consumption has highlighted the need to provide more information about a food item’s origin, production, packaging, and distribution processes.
Need to Boost Production
Innovation can also help boost agricultural produce and reduce food shortages in several parts of the globe. This is a key role of data science in agriculture.
Use of Data Science in Agriculture
The following are some major areas where data science proc\ves to be immensely helpful:
Mapping soil and crop – digitally
This is majorly helpful for farmers with huge areas of land. In such cases, it is difficult for them to analyse every unit of land and soil to check its texture, nutrient content, and so on. It would be a tedious task. This is where data science and technology comes into play. Digital maps could be but for the same. These when combined with satellite images and weather stations could give comprehensive data to the farmers regarding their land. It could also help them project future possibilities to a certain extent.
Predicting and analysing weather changes
Climate change is an issue that has to be taken seriously. This in turn has started affecting the daily weather patterns. Farmers are affected and hurt by these changing weather patterns all around the year. Unpredicted weather changes damage crops and affect the soil sometimes causing them to erode. Some weather-sensitive crops are affected by the sudden changes in weather and thereby their yield is also affected.
There are various tools built on data science and technology which could be used to identify the patterns in the weather change and their related relationships. This could give important insights that otherwise stay hidden.
Providing useful insights to help fight food scarcity
Food scarcity is a worldwide concern. By the looks of it, it could only grow further. The only way to minimise the growth rate or to optimistically stop it completely is to make sure the world has an excess of food availability. This excess should be calculated after considering every single citizen of the world.
Data science in agriculture, along with technology, could help in boosting the overall food output and yield through various feedback mechanisms and data modeling techniques. This could help in containing food scarcity at the global level.
Managing pests and other crop diseases
Pests are the main source by which a farmer’s profit margin takes a hit. Data science and modeling uses advanced algorithms that could help in estimating the pattern and behavioural changes of pests and related microscopic diseases.
Managing and recommending required fertilisers
Fertilisers to be used vary with the nature of the soil, the crops, the water availability, and the climatic conditions. It is difficult to keep track of all these factors, their relationships, and their ever-changing nature for large areas of farms. This is where data science comes into play. Advanced algorithms are built on this which can handle all possible permutations and combinations.
Managing water and irrigation through automation
Water is a prime issue in all parts of the world. It is scarce for drinking purposes let alone for farming and irrigation. Data science and technology helps in reducing the usage of water based on the crop, weather, and other factors. The constant feedback provided by these data models helps in understanding the water situation in a much better sense.
Identifying Agricultural Niches
The use of data science in agriculture helps in identifying patterns and relationships that may otherwise remain hidden. For example, information related to the positive impact of minerals on the metabolic functions of poultry and livestock, or the benefit of carotenoids can be used to change agricultural processes for the better. The quick and efficient processing of a vast amount of data yields several useful pieces of information that can be used to make the agriculture sector more efficient and productive.
Using Satellite Imagery
The use of satellite images of crops and land can help farmers get prompt updates about the true condition. This helps the prompt identification of problems, if any. Satellite-based crop monitoring enables quick inspection of areas besides showing the true weather conditions for farming.
Optimising Farm Equipment
The integration of sensors in farming equipment and the deployment of big data applications can help farmers to optimise farm equipment. Constant monitoring can enable farmers to receive fuel refill alerts or service maintenance dates and other crucial information to boost productivity.
Managing Supply Chain Issues
A significant proportion of the food produced for human consumption is wasted annually due to storage, weather, and other factors. Such wastage can be reduced by using big data in agriculture to deal with supply chain inefficiencies. Data science can help bridge the gap between demand and supply for food.
All these when implemented in conjecture, enables something called Smart Farming. Simply put, it is the automated management of the entire agricultural/farming process, from end-to-end.
Now, like with every other process, using data science for agronomy has its own set of challenges.
Challenges faced while using Data Science for Agronomy
While data science can help the agriculture sector immensely, its usage and implementation are hindered by several factors, including the unwillingness of the farmers to accept it. Some major reasons for farmers not readily welcoming the use of data science in agriculture are:
Farmers are unwilling to change their farming methods based on information provided by data science for fear of incurring high losses if things go wrong. Since shifting to digital methods requires a large investment, it is out of the reach of small farmers.
Uneducated and small farmers are not capable of understanding and analysing the data provided to them. This acts as a deterrent to the adoption of digital farming.
Collection of Agriculture Data
A major problem with the collection and analysis of agriculture data is that it comes in various forms and at different intervals. While some data, like the PH value of soil, can be taken any time, others like checking the ripeness of fruit can be done only at a specific point in time. Also, this data needs to be aggregated such that it can be compared with standard data of a similar nature. Big data analytics in the agriculture market will be valuable only when farmers share their local data with others.
Other major challenges faced while using data science to leverage and improve the agriculture process include scepticism in changing the age-old traditional habits, data collection, cleaning and storing and internet connectivity in rural areas.
Real-Time Applications of Data Science based Agronomy
This topic is not just a theory. There are practical applications for this in existence right now. All those users are getting benefitted from data science in more than a single way.
The following are a select few of them:
- In Egypt, farmers use advanced water sprinklers (with in-built data modelling technology) to water and irrigate their farms using the river Nile. These are used for medium-large farms as well.
- There are a variety of apps developed for the sole purpose of helping in agronomy. Right from choosing the right feed for their livestock to choosing the right type of fertilisers, the correct level of pesticides, the right amount of water to be used to the right type of seeds for the land, everything is suggested by technology-driven applications. FamGraze is one such example.
- Modern transportation facilities built on data modelling technology helps in the quick and efficient transport of goods between places. This is particularly helpful in the agriculture sector, as almost all of the yield goods under consideration are perishables. These need to be transported as efficiently as possible.
Case Studies on Big Data Analytics in the Agriculture Market
Several companies across the globe are using big data analytics to solve issues plaguing the agriculture market. Here are some examples of how big data can take agriculture to the next level:
Big Data Analysis to Improve Yields and Profits
Digital Transmission Network (DTN) is a unit of Schneider Electric that provides agricultural information solutions and market intelligence. The information helps farmers and commodity traders to access the latest weather and pricing data, which allows them to better manage their business. The unit found it very costly and challenging to connect its ERP systems with financial applications, agronomy packages, and sensing applications. So, it invested in a data integration tool for easy consolidation of data collected from different sources. As a result, DTN can now combine critical data to give accurate forecasts that help farmers to improve their yields and cut costs.
Processing of Huge Amounts of Data to Optimise Decision Making
A subsidiary of a leading agricultural cooperative group in France provides agronomic information software to a large number of cooperatives and merchants in the country. SMAG, the subsidiary of InVivo, wanted to combine its massive data consisting of weather-related information, images taken from satellites and drones, and information on soil types. They aimed to help the farming community make informed decisions to solve the country’s food challenges.
The unit developed a complex agronomic algorithm to process the vast amount of data accumulated over the years. This algorithm is being used to optimise decision making by allowing users to track the progress of their crops and predict their yields. The algorithm is currently being used to manage wheat cultivation in France, but the company plans to expand the use of big data analytics to the entire agriculture segment.
Being the only country in the world known for its agricultural practices, India should feel proud of itself. At the same time, the farmers must evolve with the changing technology and seek its help in improving their agricultural processes, practices and finally their yield.
Modern data modeling techniques using data science could be used prolifically to improve the agricultural output and thereby the living standard of the farmers and in-turn the whole country that rest of agriculture.The Data Science program offered by Geeklurn could help in a deeper understanding of the topic discussed in this article.