‘Data Science’ itself reflects its property by handling data scientifically. No wonder why data science exquisitely landed almost in every field and now it boldly entered agriculture. Additionally, data science agriculture is at boom but the main question arises does the farming industry understand its importance?
While, in 2021, as per reports that says agriculture share its contribution of 19.9% or 20% while the other sectors slid landed. Earlier this percentage 17-19% was seen in the year 2003-04. The positive growth clicked the pendulum and showed an increase of 3.4% at positive prices.
Even so, agriculture remains in phobia conditions where many heaping disasters affect it badly for example – unpredictable monsoon, water retention in soil, lack of drought conditions, floods, climate change, migration, and pests.
People or farmers involved in agriculture are least bothered. Whereas, government or private institutions failed to provide schemes and loans to farmers. Because of this, farmers are certainly starting to lean towards the search for better-paying jobs.
Certainly, technologies came forward to make a revolution in farming. Data science is one of them. Let’s roll over to the primary roles of Data Science and understand why data science can be revolutionary for agriculture.
Data science agriculture coping with Climatic or Monsoon Change
Indian agriculture was totally dependent on canal systems, underwater pumps or rain irrigation systems. But, aberration of weather can cause damage to crop transportation, storage and soil erosion.
Indeed, the hidden changes got elaborated by data science that helps to identify the patterns and relations in agriculture. This can be attain by using ML & IoT – Geotech monitoring techniques and instrument, IoT enables the sensors which measures the pressure, moisture and temperature of the specified area that enables them to draw conclusions.
Similarly, agriculture has now started shifting towards databases which can bring a tremendous change. To explore in-depth monsoon prediction techniques, YouTube is the best platform where Machine learning for monsoon prediction. As well as Mr Ranveer Chandra – The Managing Director for Microsoft Research, and the CTO of Agri-Food at Microsoft tells us in TED talks.
Yield Prediction or Digital Soil or Agriculture Niche or Precision Farming.
In contrast to data science agriculture, countries like Ireland have adopted satellite-based soil or precision farming techniques. This leads them to decide what crops seeds must be buried to get optimal results about the quality of the soil. However, small or medium-scale farmers cannot take advantage of this technique who set one’s sights on the future.
Data Science Agriculture Recommendation Engine for Fertilizers
Experimenting with the chemical might turn the situation worse. For this reason, adding an exact and appropriate amount is easy to attain by using the Data Science Agriculture Analysis. Alternatively, data science agriculture provides the exact amount of prescription by analysing soil chemicals, land type, irrigation techniques, fertilizers characteristics, weather conditions, soil testing methods and biological properties.
Moreover, after channelizing these parameters, it finds the optimal fertilizations range. Data professional provides the right equation of fertilizers to the farmers to avoid misuse. Official GOI Recommendation Engine.
Management and Detection of PEST/DISEASE by Data Science Agriculture
Of course, Pest or disease can easily burn down the farmer’s profit. But excess use of pesticides is not a good attempt. In this case, with the use of digital tools and analysis, farmers can deal with harmful pests or insects.
Data Science agriculture can instantly understand the difference between a beneficial pest and a harmful pest. DS after an analysing provides a proper percentage of pesticide dosage that will only affect the pest, not plants.
Real-life examples are not reel life
Beyond this farmers of Egypt adopted this technology that involves collecting water from the Nile river and its sprinkles to the large farming fields of Egypt.
The consumer of Dubai effortlessly receives any fresh vegetable from Africa on the same day by modern transportation facility developed by DS.
Conclusion
The Data Science revolution is in its early period most values are still unsung. Data science is at forefront of contemporary agriculture, many tools, measures and analyses is in practice to compile farming issues.
Nevertheless, the factors that put the “smart” in farming can be, DGPS-different global positioning systems for better accuracy.
Understanding the importance of GIS (Geographical Information System), Proper use of Data Sensors, data transmitters, drones, REMOTE SENSING TECHNOLOGY, & Cloud Architecture. Where IoT fully functions to communicate with other devices that provide real-time updates. Here, this allows farmers to get the exact idea of their land and crop status.
Therefore, Machine Learning, Big Data and Data Analytics set the entire process. Their essence can be smelled on social media. They sense the requirement and implementation of the desired features thus becoming an important part of smart farming. Still, there is a hesitation about what window Data Science introduce. As a result, data science is creating aspiration by showing its embryonic behaviour by growing crops in abandon.
Very Informative and to the point….
Well done.
Excellent…