Leveraging Big Data in Agriculture

Big Data Agriculture

Across the globe more human beings are dependents on Agriculture and its commodities. Also, agriculture is the backbone of the nation’s economy and it is the responsibility of the Scientists, Industries and Governments to make the farmers’ yield profitable.

Leveraging Big Data

There are many factors that contribute to farmers’ profitability. They are, finding effective hybrids, Pesticides, Air moisture, Ground Moisture, Water availability, Temperature, Rainfall, Price forecasting, Government actions, Market Data etc.

From the above mentioned attributes Big Data framework, and Machine Learning algorithms play a key role

  • To arrive optimum decisions in farming
  • Crop recommendations, Intercropping recommendations
  • Selection of suitable Hybrids
  • Farming practices
  • Pests prediction and Management
  • Forecast the Agri commodity prices ahead of the season
  • Profitability Analysis
  • Policy recommendations

By using Big Data frameworks the huge volume, variety and veracity can be handled, and highly computational Machine Learning algorithms can be developed.

Though many benefits can be derived by using Agriculture Big Data platforms, two major benefits are discussed below – Optimized Farming and Commodity Pricing

Optimized Farming

Gone are the days when farming is done by traditional methods. Today, by using big data technologies and Machine learning algorithms many attributes can be predicted in advance and associated together such as Weather, Monsoon behavior, ground water scarcity, Soil conditions, Labor & Machinery costs, intercropping decisions and Pests’ management. By associating all these attributes optimized decisions can be taken at all phases of farming.

An agricultural big data framework is the meticulous idea to collect all sorts of huge volume of historical and near real time data related to weather, soil, satellite remote sensing images, farming costs, and local pests’ data. This frame work can handle different formats of data like structure, unstructured and images. Various decisions can be taken in advance by processing Tera and Peta bytes of data which helps farmer in saving efforts, costs and increases the yield productivity.

Below Mentioned attributes can be best utilized to derive an optimum decisions in farming:

  1. Weather: weather has a profound influence on Agriculture in terms of growth, crop yields, impact of pests and disease, water needs and fertilizer requirements. Based on the weather and rainfall forecasting information, for different demographic regions different crops can be shortlisted for selection. Also, by predicting weather and rainfall farmer can be suggested when he needs to sow, harvest, transportation and other relevant information.
  2. Soil: Minerals, ph levels, phosphorus, potassium, magnesium, calcium and moisture level data will be considered to select suitable crop.
  3. Crop cutting: By processing image sensing data it can be predicted when the machinery or labor is required to cut the crop
  4. Plant Health: Plant health can be monitored remotely by using remote sensing data
  5. Pests Management: By considering Soil, rainfall moisture, local pests patterns, appropriate decision can be taken; so that crop can be of more organic which gives good profits t farmer
  6. Intercropping: by studying historical data and current soil and weather conditions experts can suggest the farmer for altering the crop

By computing all the above mentioned attributes, optimized decisions can be taken at every phase. This ensures

  1. Ensuring profitability
  2. Low production cost
  3. Very little or no pesticide residue is ensured
  4. Reducing farmers’ risk
  5. Higher productivity
  6. Effective utilization of land, machinery, labor and time

Commodity pricing

Farmers can be befitted with 1) Forecasted Agriculture commodity prices and 2) Sharing the current prices of Agriculture commodities

Forecasting commodity prices:

It is noticed that the prices of the commodities fluctuates significantly in the semi-arid farming zones, Monsoon based farming zones and also prices fluctuates due to decisions taken by the local governments such as  MSP (Minimum Selling Price) etc

Forecasting price given well in advance for agriculture commodities is helpful in many ways.

Sowing decisions by farmers: The price forecasting information helps the farmer to know the price in advance that helps to take appropriate decision whether to sow that particular crop or not; if so how much profit he can expects

Policy decisions by Government: The price forecasting information acts as input to governments and other authorities to take decisions on Minimum Selling Price (MSP), Imports Exports decisions and in other relevant areas

Market Prices:  The prices of the yield is not same across all the local markets. So it is necessary to provide forecasted price information for local market wise, district wise, state wise and nation wise.

To forecast the Agriculture commodities it is required the past 7 to 10years of historical data for all the variety of crops. To handle this huge data and high computations, the distributed big data platform can be leveraged.  This also helps in computing the near real time data to find out the current prices of all variety of the crops.

Sharing the current commodity prices

The prices of the agriculture commodities varies across the markets. In order to avail the benefit of higher prices in the local or nearest markets the current price of the commodity should be available. This type of information can be made available for all the crops by developing applications using bigdata.

The web based or mobile based applications can be developed for the farmers’ benefit where they can leverage the maximum benefits if they sells the crop in the local market or the other markets.

This information can be passed to farmers in many ways such as

1) Automation of e-mail or sms alerts

2) Browsing Internet application or by mobile app

3) Advertising in media through media analytics.

By collecting local pricing in near real time and adding the transportation expenses, farmers can get the better prices for their crops without a mediator.

In Conclusion

Inclusion of Big Data & Machine Learning capabilities in an AgriTech system can prove to be highly beneficial for farmers. Such systems will lead to:-

  • Improved productivity with better farming practices
  • Improved Production with timely decisions
  • Commodities forecasting in various markets

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