Data Science in the Indian Agriculture Industry
Agriculture is the backbone of the Indian economy, but the industry currently needs more support than any other. India is a country of over a billion people in population, out of which, over 70% of the population lives in the rural areas. With 40% of the country’s workforce, agriculture is a major industry and an influencer of the Indian economy. Despite this, its contribution to the $2.3 trillion economy is just a meager 16% of the entire GDP.
Agriculture in India lacks institutional attention, support from banks in terms of loans and farmer welfare schemes, and suffer from a myriad of disasters like depleting groundwater levels in rural areas, climate change, unpredictable monsoon or lack of it, droughts, floods, unfair price fixing policies of produce, migration of farmers towards the cities in search of better paying jobs, and more.
Agriculture is one sector responsible for feeding every individual, but the people involved in it are the last to be taken care of. After failing institutions, time has indeed come for technology to take over the change. With newer problems cropping up every day in the most inevitable indigenous sectors, it is high time we resort to emerging technologies for solutions.
Table of Contents
- Big Data to the Rescue
- What is Smart Farming?
- Components of Smart Farming
- What is Precision Agriculture?
- Career as a Data Scientist in Agriculture
- Challenges in the Indian Agriculture Scene
Big Data To The Rescue
The revolutionary technology that goes by the name Big Data has already made waves in other Indian industries from IT to healthcare. And now, investors and market players are planning to leverage the potential of Big Data for the benefit of agriculture in India. Apart from major companies, it is the vision of several youth of the country that has attracted the use of Big Data for farming. For instance, SatSure, founded by the 33-year-old Abhishek Raju works on using Big Data and its allied technologies like data science and IoT to better the lives of farmers.
Abhishek shares the fact that he was deeply moved by the rate of farmer suicides and the lack of application of science and newer technologies in the oldest Indian industry – agriculture. His solution to this is SatSure. According to him, “The parameters associated with soil health and crop growth have had a very restricted scope for research and his technology immensely uses Big Data and Machine Learning technologies to solve the restriction and bring about insights on crop phenology.”
When we got on a call with Mr. Abhishek Raju, he shared that, “Indian agriculture sector is very disorganized and heavily cash oriented. Electronic transactions are almost non-existent, and that is why most of the transactions are unrecorded. We help them provide insights about farm productivity, when to irrigate, sow, harvest, and the patch of land that can be used by farmers. We help banking and insurance companies in settlement of risk assessment, crop loss, and offer insights by analyzing current and historical satellite images.”
“The satellite images are not only in a single visual spectrum but have multiple data layers which contain images merged into one to gather as much information as possible. This is what we at SatSure mine. However, data is one thing, and what you infer from data is another thing. We analyze data to make action oriented conclusion-able intelligence.” He adds.
Facilities like satellite-based filed monitoring, embedded sensors on crops and fields, predictions on wind direction, fertilizer requirement notifications, pest infestations, GPS-enabled tractors, water cycles, and more are acting as points of rich data sources that could be used for better agriculture practices. Besides, Big Data and analytics now also enable monitoring and supervision for growth rate and nutrient requirements on a plant-by-plant basis. Moreover, analytics is enabling farmers to make data-based decisions like which crops to plant for their next harvest. The rich information on soil health, water availability, and predictions on rainfall and precipitation make this data source. Welcome to the world of Smart Farming.
Satsure enables insurance companies, banks, traders, pesticide and seed manufacturing companies, and farmers the ability to take informed farming decisions by leveraging the combined potentials of technologies like –
- Cloud Computing
- Big data
- Machine Learning
- The Internet of Things
- Web-based Software as a Service platforms
Raman Singh Saluja, founder of Gramco Infratech says, “Agriculture is a very physical business, requiring physicality in terms of handling/warehousing/value addition / etc, which will continue to be the bedrock.”
When it comes to the use of technologies, he shares that data analytics offers tremendous potential for improving cost to output ratio, reduce/optimize Input usage, increase yields, offer timely actionable information and do more.
He further reveals ,” At Gramco, there are two initiative underway which will be brought to market by the 3-4th quarter of 2018. One has been piloted with very encouraging results with on ground support of a leading insurance company.”
What is Smart Farming?
Smart Farming is the breakthrough application of science and technology in the field of agriculture. Smart farming is the application of technologies like IoT, Big Data and analytics on an agricultural field. It makes use of technologies like the Internet of Things, cloud computing, Machine Learning, and Big Data to enable farmers to have more insights on the consequences of their actions and take a much better and informed decision on farming practices.
The power of smart farming lies in the fact that it goes beyond solving the shortcomings and pitfalls of agriculture. The application of Big Data is leaving significant impact on the entire realm of supply-chain, giving predictive insights on farming practices and operations, help redesign business models, deliver realtime decisions on operations and more.
Jyoti Vaddi of Cropin shares, “The world population is estimated to cross the 10 billion mark by the middle of this century. This population growth combined with urbanization will require the agricultural production to double. To succeed, Jyoti recommends the need for smart solutions for fairly produced, sustainable food, feed, and fibre, which is one of the mainstay principles of CropIn.”
During their market opportunity study, Cropin gathered that agribusinesses had minimal and outdated technological/digital resources, and were not able to make informed data-driven decisions. She reveals that with consumers keen to know the origin of their food and how it was produced and processed, there was a need for transparency along the end-to-end agribusiness supply chain.
The technologies that power Cropin in delivering efficient farming solutions to stakeholders in its network include:-
- The use of app-based data generation and extraction
- Data storage on the cloud
- Satellite Monitoring
- Machine Learning and Real-Time data visualization
These technologies foster an environment for production forecast, risk management and coverage, output predictability, quality maximization, and increased farm sustainability to agriculture input companies, banks and financial institutions, insurance companies, farming enterprises, seed manufacturing companies and government bodies respectively.
Components of Smart Farming
Smart farming is a network of interdisciplinary and complementing technologies and facilities. The components of smart farming are best if they comprise of the following:
Management Information Systems
This is generally the database where all chunks of data from multiple sensors and resources are gathered, stored, analyzed, and retrieved for actions. An optimized management information system should offer information on:
- Crop stress
- Statuses on crop tissue nutrients
- Crop population
- Weed patches
- Fungal or insect infestation
- Crop yield
- Physical condition
- Soil texture
- Nutrients and more
- Wind speed
Technology is what puts the smart in smart farming and the following make up the network:
- Global positioning systems and differential global positioning systems for better accuracy
- Geographical information systems
- Remote sensing technologies like data sensors, RADARS, data transmitters, drones, cameras, and other connected devices
- Cloud architecture
- The Internet of Things, where devices are capable of communicating with each other and deliver real-time updates and notifications to farmers on crop statuses, water levels, moisture content, crop yield, and more.
Technologies like Machine Learning, Data Analytics, and Big Data for the entire process and setup to make sense
What is Precision Agriculture?
Also referred to as Site-specific Crop Management System or Satellite Farming, this is a concept in farming that relies on observation, measurement, and response to various inbound and outbound requirements in agricultural fields.
The primary vision of precision farming is to optimize RoI and preserve resources by allowing farmers and landlords to take optimized and informed decisions from the available field data. Precision agriculture fosters an environment where farmers can zero-down precise locations in their fields for the spatial availability of several resources like water availability, topography, soil fertility, organic matter, nitrogen levels, moisture content, the presence of magnesium, potassium, and more.
Complemented by services and features like GPS devices, sensors that are even capable of measuring chlorophyll levels, drones, and satellite imagery, precision agriculture offers a treasure chest of information for farmers.
Career as a Data Scientist in Agriculture
The science of agriculture is a very complex field and is interdisciplinary. It uses the fundamentals of chemistry, physics, math, statistics, biology and economics and business management. The scope of the agriculture scene in India is still in its developing stage and requires niche experts with versatile skill sets to bring about changes.
The role of a data scientist in agriculture is very similar to that of in the roles and responsibilities in other industry. However, exposure to wings like plant science, plant biotechnology, soil science and animal science will help aspiring data scientists to create an impact in the field and allow them to make more sense out of the clusters of unstructured data from multiple resources.
As far as the technologies deployed in agriculture analytics is concerned, you can divide the requirements of them into the following:
- Data Capture: IoT Work involved with sensors, open data, biometric sensing, reciprocal data, and genotype information
- Data Storage: Cloud-based solutions and platforms, Hybrid storage solutions, Hadoop and Hadoop file systems, Data Lakes
- Data Transfer: Cloud-based wireless platforms, linked open data
- Data Transformation and Analytics: Normalization, Machine Learning algorithms, Cognitive computing, Ontology based Decision Support Systems for Planting instructions, Yield models, and Benchmarking solutions
- Data Marketing: Data Visualization
Challenges in the Indian Agriculture Scene
Though the technologies are efficient, proven to work, and revolutionary, one of the major challenges lies in their application in the Indian agriculture sector.
Problem is not when there are no technological solutions to farming concerns, but not having a proper application of them is a bigger concern. Mr. Hemendra Mathur, Managing Director, SEAF India Investment Advisor, shared that his interactions with farmers from Himachal, Madhya Pradesh, and Rajasthan made him come to a conclusion that farmers today are ready to embrace the new technologies for better farm economics. However, there is the need to educate them on risk mitigation and potential upsides probable with the use of data.
He shares a real-time incident wherein a pilot project conducted by an ag-data company made farmers realize that their estimation of their farm areas were completely different from the estimation arrived at after geo-tagging and that they were able to work on input application better after knowing information to the points.
This also makes us realize the amount of hours of training that has to be given to the farmers in the implementation of these technologies in everyday farming.
This involves training on the use of the devices, basic troubleshooting, use of data, use of smartphones and app, and more. The concerns don’t end there. Problems like infrastructure, need for uninterrupted power and internet connectivity, and finance to deploy the technology are always a concern.
Though opportunities for data analytics in the field of agriculture in India are aplenty, the best use case of it is yet to happen. With the visions of those like Abhishek Raju, Jyoti Vaddi, Raman Singh Saluja, and others gradually taking shape, we can be sure that in the coming years, farmers will see better days in the farm and their harvest.
About the Author
Shweta Gupta, is currently VP, Digital Vidya, contributing towards building technology equipped youth for solving problems in the era of data as part of Digital Vidya’s mission in building skills and reskilling the existing workforce in the technology fields and specifically Data Science.
She has 19+ years of Technology Leadership experience, hold a patent and number of publications in ACM, IEEE and IBM journals like Redbook and developerWorks. She has been speaker at technology events like IBM Commerce Global conference (Amplify), Regional Technical Leadership Exchange, Society of Women Engineer (SWE).