Agriculture plays a significant role in India’s socio-economic sector. At the same time, it is demographically the broadest sector compares to other economic sectors of India. Agriculture vastly depends on the climate, soil, irrigation, cultivation, harvesting, pesticides, rainfall, and many other factors. However, India grapples with many challenges associated with agriculture, such as climate change, extreme weather conditions like floods and droughts, scarcity of groundwater resources, etc. Besides, the people involved with direct agriculture work are the last to be taken off.
So, it is high time to sort out these problems with emerging technologies like Big data analytics in agriculture, IoT, etc. Not only that, but advanced technologies like Big data analytics can also explore and show hope to the problems like timely compensation pay off, losses incurred, market accessibility issues, and various similar issues.
Furthermore, there are two other factors which are helpful for the government and the farmers in decision making namely:
1. Historical crop yield record with a forecast that reduces risk management.
2. Policymaking of the government for supply chain operation.
In the agriculture sector, agribusinesses and farmers need to make many decisions every day and handle complexities associated with various agricultural work. Here accurate yield estimation plays a critical role in the planning. Data mining techniques help in accomplishing effective and practical solutions for this problem.
Considering all these factors, big data in agriculture has become an obvious target for big data analytics. As a whole, with the help of big data in agriculture in India, the farmers can use the following information in a more relevant way:
- Environmental conditions
- Variability in soil
- Input levels
- Combinations and commodity prices
Related post - What is Supply chain analytics?
How Big data analytics works technically in the agriculture sector?
This is a phase by phase process where the role of IoT devices cannot be ignored as well.
In the first phase, IoT devices collect the data. Sensors plugged in trucks and tractors and fields, plants, and soil aid the real-time data in the collection directly from the ground.
In the second phase, data analysts integrate these large amounts of data collected and other information available in the cloud, which includes weather data and pricing models to determine patterns.
Finally, based on the revealed patterns and insights, it helps in controlling the problem. With big data analytics, we can pinpoint existing issues, like soil quality, operational inefficiencies, etc., and formulate predictive algorithms. These are finally implemented as an alert to prevent future problems.
Role of Big data in Agriculture
1. Useful data collection to fight food scarcity and empower farmers
Data scientists use analytics tools to analyze and process a massive amount of data collected using sensors. These data show whether agricultural investments in the country are paying off and help develop the farmers' policies. This project aligns with the United Nations’ Sustainable Development Goals targeting the goal to double the farmers' agricultural productivity and incomes and help them.
2. Managing Pests and Crop Diseases
Agricultural pests are a big reason to cut into a farmer’s profits. Pesticides are used to reduce this risk. However, the overuse of pesticides can have adverse effects on plants and all living things. Fortunately, big data analytics in agriculture resolve this issue efficiently. Using the correct analytics tool, data scientists can help farmers analyze how much pesticide should be applied. For example, one of the companies named Agrosmart using IoT sensors and artificial intelligence can determine insects and their quantity present in a crop. Now, based on the analysis, they can formulate a pest management approach. This is another way cost-effective pest control approach.
3. Identify hidden patterns and relationships
Big data in agriculture in India has an undeniable role in identifying patterns and relationships that may otherwise remain hidden. Data scientists use various tools for this purpose. This, in turn, push agricultural science to move forward and conclude specific factors. For example, scientists know trace minerals affect poultry and livestock's metabolic functions, while carotenoids increase egg yolk quality and nutrition. These small factors bring substantial change in the agricultural sector.
4. Help to cope with Climate Change
Climate change is a looming concern in the Indian agriculture sector. However, data scientists use various big data analytics techniques to overcome this situation. For example, using IoT sensors, rice farmers can collect vital information about crops. These data help farmers to optimize production cycles even under exceptionally adverse climatic conditions. Big data analytics scientists can also scrutinize soil data that improve farmers' understanding of how soil contributes during climatic changes.
5. Yield Predictions
A poor yield can cause a devastating season for all the entities that depend on the crops. Utilizing big data analytics, yield can now be estimated months in advance. This reduces unpleasant surprises for farmers and other agricultural professionals. Besides, using satellite data end of the season can be accurately predicted based on real-time data.
6. Automated agriculture
Big data analytics has a crucial role in automated farming or precision agriculture. With the intervention of the internet, drone technology, and data analytics, automated agriculture has reached a new height. Today, farmers can use drones with advanced sensors to survey their farming, update the farming data, and analyze the improvement.
7. Advanced Supply Tracking
The supply chain is closely related to agrarian activities where products like raw material, food, animal feed, livestock, chemical, fertilizer, pesticides, paper, and seeds are used. So, historical data matters here. So, for production scheduling, accurate estimation of crop production and associated risk analysis helps the agriculture sector plan supply chain decisions. Big data can resolve some of the problems in the supply chain, as it provides more oversight related to harvest.
8. Risk Assessment
Though risk assessment is a normal protocol in general business, that’s never been possible in the world of agriculture until now. However, data-driven risk assessment affords a lot of benefits. Big data analytics in agriculture risk analysis is now easier. Using real-time data now, farmers can ensure minimal damage.
Challenges associated with Indian Agriculture Scene
Though we have discussed all the possible efficient and proven ways of working on big data in agriculture, one of the major challenges arises in their Indian agriculture sector application.
Not having a proper application is the biggest concern in the farming sector. Farmers are ready to embrace new technologies; however, the application is enough, but educating them properly is the need of the hour.
Proper training and education on the use of the devices, use of data, basic troubleshooting, smartphones and apps, and more is necessary. There are more concerns there. There are infrastructural issues, interrupted power supply in rural areas, and lack of internet connectivity. In addition to that, there is a scarcity of finance to deploy the technology. Though opportunities for big data in agriculture in India are plenty, the best use cases are yet to happen.