‘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. 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 shown an increase of 3.4% at positive prices.
Still, agriculture remains in phobia condition where a number of heaping disasters affect it badly. Such as 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.
Cope up with Climatic or Monsoon Change
Indian agriculture is totally dependent on a canal, underwater pump or rain irrigation system. But, aberration of weather can cause damage to crop transportation, storage and soil erosion.
Relatively data science found a way to identify the relations and patterns that might be hidden from others. This can be attained by using ML & IoT – Geotech monitoring techniques and instruments, IoT enables the sensors which measure the pressure, moisture and temperature of the specified area that enables them to draw conclusions. Agriculture is now started shifting towards databases this can bring a tremendous change.
Monsoon prediction technique using ML by Bek Brace
Yield Prediction or Digital Soil or Agriculture Niche or Precision Farming
Countries like Ireland have adopted satellite-based soil or precision farming techniques. This leads them to decide what crops seed must be buried to get optimal results with reference to the quality of the soil. Though small or medium scale farmers cannot take advantage of this technique who set one’s sights on the future.
Recommendation Engine for Fertilizers
Experimenting with the chemical might turn the situation worse. Adding an exact and appropriate amount can be attained by the Data Science analysis. DS provides the exact amount of prescription by analysing soil chemicals, land type, irrigation techniques, fertilizers characteristics, weather conditions, soil testing methods and biological properties.
After channelizing these parameters, it finds the optimal fertilizations range. DS professional provides the right equation of fertilizers to the farmers to avoid misuse. Official GOI Recommendation Engine (Click image to obtain the link).
Management and Detection of PEST/DISEASE
Pest or disease can easily burn down the farmer’s profit. But excess use of pesticides is not a good attempt. With the use of digital tools and analysis, farmers can deal with harmful pests or insects.DS 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 not reel life
Farmers of Egypt adopted this technology that involves collecting water from the Nile river and its sprinkles to the large farming field 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. DS is at forefront of contemporary agriculture, many tools, measures and analyses are done to compile farming issues.
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. This allows farmers to get the exact idea of their land and crops status.
Machine Learning, Big Data and Data Analytics set the entire process. They sense the requirement and implementation of the desired features thus becomes an important part of smart farming. Still, there is a hesitation that what window Data Science bring in. Data Science is creating aspiration by showing its embryonic behaviour by growing crops in a desert.
References
http://statisticstimes.com/economy/country/india-gdp-sectorwise.php
https://www.economist.com/node/21698612/help/accessibilitypolicyhttps://medium.com/trends-in-data-science/data-science-in-agriculture-bd172cf5cdb9
