Data Science in Agriculture – Advancing Together & Benefiting Farmers

Data Science in Agriculture – Saving the lives of farmers

Agriculture is the most important sector of the Indian economy, that provides employment to almost half the population of the country’s workforce!! India is the second-largest producer of fruits and vegetables in the world.

Agriculture is the backbone of Indian economy, still, it suffers from a heaping number of disasters such as climate change, unpredictable monsoon or lack of it, droughts, floods, migration of farmers towards the cities in search of better-paying jobs, and more. People involved in agriculture are the last to be taken care of, even when they are the one who feeds the whole country.

With institutions failing to support agriculture in terms of providing loans and farmer welfare schemes, the time has come for technology to take over the change. Data Science is here for the rescue!

Data is the necessity for industries and hence, data science has a number of applications. After revolutionizing industries like IT, Banking, Manufacture, Finance, Healthcare, and many more, it is all set to benefit the agriculture industry.

To know how data science is used in all above-mentioned industries, check Data Science Applications

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Data Science Applications in Agriculture

Here are the six applications of data science in agriculture sector:

Data Science in Agriculture

1. Digital Soil and Crop Mapping

This is related to building digital maps for soil types and properties. Some people in the agricultural industry manage so many acres of land, it’s almost impossible to get prompt updates and alerts about potential problems without help from technology.

Farmers Edge, a Canadian company takes daily satellite images of farms and combines it with other relevant data. It includes information from more than 4000 interconnected weather stations!

Several countries like Ireland also depend on satellite-based soil and crop monitoring to inspect areas more quickly than traditional methods allow. This helps in deciding what crops should be grown on a particular piece of land. It saves a lot of time and effort and results in higher yield production.

2. Weather Prediction

Weather plays a very important role in agriculture production and has an influence on the growth, development, and yield of crops. Weather aberrations can cause physical damage to crops and soil erosion. The quality of crops from the field to the market depends on the weather. Bad weather can adversely affect the quality of crop during transportation or storage.

Data science experts know how to use tools that identify the patterns and relationships that may otherwise be hidden. They can draw conclusions that push agricultural science forward through the examination of specific factors leading to change in weather. The findings brought about by sifting through databases and studies to conclude things like this in agricultural processes can bring about remarkable changes.

Elements of agricultural weather forecasts are:

  • Amount and type of coverage of sky by clouds
  • Rainfall and snow
  • Max, min, and dew point temperatures
  • Relative humidity
  • Wind speed and direction
  • Low-pressure areas, cyclones, tornadoes, and depressions
  • Events like fog, frost, hail, thunderstorms, and wind squalls

3. Fertilizers Recommendation

Knowing the exact fertilizer rate is a science and requires a thorough analysis of multiple factors. Often, hundreds of dynamic parameters have to be considered. Such parameters include crop nutrient uptake rates, research data, soil chemical, physical and biological properties, weather, water composition, land type, soil testing methods, irrigation techniques, fertilizer characteristics, interactions between fertilizers and many more.

Because of the complexity of finding the “optimal fertilization range”, misuse of fertilizers is a global phenomenon. The majority of farmers still rely on trial and error, guesswork and estimation. The result is, crops do not meet their yield potential, and increase environmental pollution. Data science professionals are now able to advise the farmers with the right quantity of fertilizers.

4. Disease Detection and Pest Management

In modern agriculture, advanced algorithms are used to identify the patterns and behavior of nature that helps in forecasting the invasion of pests and the spread of microscopic diseases. Advanced analytics in agriculture are informing how farmers should manage pests. Digital tools and data analysis in agriculture are being utilized to scientifically deal with harmful insects.

Agricultural pests can quickly cut into a farmer’s profits. But, misusing pesticides can have adverse effects on people, plants and other living things. Fortunately, some companies have recruited data science professionals to develop user-facing platforms that analyze when to apply pesticides and how much to use.

While some insects can be incredibly beneficial to farmers and the crops, others can be toxic and spread diseases. Disease detection can be done by taking images of the field using drones and processing them to detect areas within this field that are infected.

5. Adaptation to Climate Change

Climate change is a looming concern that has already affected the agriculture sector. However, data science experts are working hard to figure out ways to compensate for the change.

One project involves giving IoT sensors to Taiwanese farmers of rice production so they can collect information that is necessary about their crops. It will help farmers to optimize their production cycles, even if climatic changes make it challenging. The traditional farming calendar is no longer sufficient due to the extreme climate changes but, data analysis can revolutionize the future of farming.

Data scientists are also analyzing agriculture soil data to understand how soil can cope with climate change by releasing greenhouse gases and also how soil can adapt to climate change.

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6. Automated Irrigation System

We can also use weather prediction in the automated irrigation system. How exactly? Having a system that tells you the soil is dry but you don’t need to irrigate because it is going to rain after a few hours seems to be an interesting use case.

All the countries in the world are currently in a situation where they are required to use water in a very efficient manner. According to the recent studies, water is becoming more and more in short supply worldwide and more than one-third of the world population would face total water shortage by the year 2025.

In agriculture too, the major problem which farmers face is the water scarcity, so to improve the usage of water, one of the irrigation systems- using drip irrigation which is implemented as Automated irrigation system for small scale farms. Another irrigation system- Automated irrigation system using weather prediction.

Any queries in data science agriculture applications? Share your views in the comments.

Data Science Case Study on DATOS Project

According to a recent press, DATOS project applied artificial intelligence, machine learning, and other data science techniques to remotely sensed data. Apart from that, it used systems to produce geospatial outputs that can be used for disasters, agriculture and other purposes.

The DATOS Project has developed a way to map out crops by using satellite images and by extracting the temporal signature of crops determined through radar satellite images.

Moreover, DATOS produces flood situation maps by retrieving satellite images and letting AI identify flooded areas from these images. It is able to detect floods in areas that are hit by heavy rainfall and sends these mapped out areas to the respective DOST regional offices in the event of severe weather disruptions.

Data science is also used to detect objects from satellite images. An example is the current partnership with the Bataan Peninsula State University in order to help the institution automate the mapping of their province’s mango trees.

Other objects that have been detected from satellite images include road networks, ships, land cover classes, and built-up areas.

Real-life Examples of Data Science in Farming

  • A good example is in Egypt, where farmers use water pumps to collect water from the river Nile to their crops. Advanced water sprinklers are used to irrigate large fields and this helps the crops to get enough water which is essential in their growth. This method is useful in the efficient usage of water.
  • The invention of the mobile app such as “FamGraze” for farmers manage their grass more effectively by suggesting the cheapest feed for their livestock. It saves time and there is no need for any paper or spreadsheets. This helps in working faster and being accurate in and off the field!
  • Modern transportation technology facilities help farmers easily transport fertilizers or other farm products to different parts of the world. For example – consumers in Dubai will get fresh carrots from Africa on the same day when the carrots leave the garden in Africa.

Summary

These were just a few cases showing how one can use data science in the work of an agricultural corporation right now because, in the future, we cannot predict what opportunities it can bring. Technology has played a huge role in developing this industry. Today, it is possible to grow crops in a desert by use of agricultural biotechnology and there is much more scope in the future.

It’s the time for knowing the Role of Data Science in Weather Prediction

Any other use of data science in agriculture that you want to add? Share them in the comment section.

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