How Big Data Can Revolutionize Agriculture

by October 15, 2017 0 comments

Big data is becoming pervasive by introducing more sophisticated ways to exploit roots of technology. Not only user interfaces but also necessary tools have evolved drastically. Big data has made the world truly close, and yes, the customised data choice is a cherry on the cake. Big data tools and its results have entered into almost every segment of human lives. Just say the name and big data is there. Actually, data is everywhere, it needs to be handled professionally to take out gold from ash.

The agricultural segment is the backbone of the Indian economy. Not only India, the existence of humanity is having a knot with the yield from the land. The world is changing, things are changing, the climate is changing, and humans have already adopted those changes. But the motherland hasn’t. According to a survey, the world population will be having a boom real soon by hitting around 47% growth by 2040. Now that’s a warning bell for human existence. The overexploitation of natural resources and lack of strategic decisions has led all of us to a situation where the balance of nature has shifted to a whole new different level.

To tackle the future food crisis, technology has to be used to analyze and modify the existing agricultural practices. Here, big data comes into the picture. Let’s have a quick overview of the ways in which big data can be deployed to evolve agricultural segment.

 

1) Generation of Data Sets by Revealing Food Systems

Data has enormous power to turn things upside down, but only when it is used effectively. The data can only be used wisely if it is converted into segregated data sets. The agricultural segment has a long list of attributes that can be taken into consideration for the proposed analysis and consequent result studies. Key attributes having the impact on the process output can be handpicked and used for generating data sets. These data sets will be used to produce a ground for all related activities. Every food systems have different structure and these can be easily analysed only and only if, the data set implementation is done.

 

2) Monitoring of the Trend

All the data relating to the history of specific crop disease or pest can be used to generate the data set and consequently monitoring of this data may lead to unfolding the trend in the agricultural field. Nowadays, predicting exacts things are nearly impossible. All the attributes have become so arbitrary that nothing can be guaranteed. But monitoring these attributes, for instance, the pest and crop disease history, data monitoring can be used to predict the future attacks on yield so that preparatory actions could be taken. This will not only save the stakeholders money but also the time investment. Thus, monitoring the selected attributes has an enormous importance in the implementation of big data.

 

3) Impact Assessment

Every system is designed with the consideration of risk analysis. Every wrong turn has to be considered before it is taken. The probable impacts and corrective actions for the same have to be defined. Same goes with the agricultural segment. Today, there is a number of unfortunate situations where the whole yield in the field is wasted due to some uncertainties. These things can be managed well if the impact assessment is done properly. For instance, if the impact assessment for pesticides is done at the very first stage of sowing the seed then the probable failure can be prevented. In any unfortunate situations, if the pesticide turns out to be dangerous, then the impact analysis helps to avoid the consequences. Necessary measures can be taken to avoid the wrong turns and help in taking corrective actions.

 

4) Data-Driven Farming

As per the current scenario, decision makers are facing tremendous problems in predicting probable failure. Here, data is the saviour. Data can be used effectively to conclude predictions thus preventing them in taking risky decisions. Today, data sources including satellites, mobile phones, weather stations have contributed in making this possible. For an error proof analysis, the data quality and variance is a must thing. And the data source serves for both of the necessities. What to plant? When to plant? These basic questions can be answered very easily if the data backs it up. The dream of data-driven farming is slowly making its move and proving it with improved yields.

 

Summary

Big data has evolved the way things work. Now, it’s a turn for the agricultural segment. Many researchers are toiling their nights to make it more and more accessible, dependable and of course yieldable. Today, agriculture segment need to evolve to preserve human existence on the earth, and undoubtedly big data can help do this. The above-noted steps can be genetically followed to develop and implement procedures to yield good results. Hopefully, the near future will evidence the utopia in agriculture backed up with green evolution.

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