Transformation Of Indian Agriculture Through Big Data And AI

By Arunav Goel for NeemTree Agro Solution

Big data and Artificial Intelligence are two of the most revolutionary technological advancements in the recent times.
Big Data is larger, more complex data sets, especially from new data sources which just cannot be managed by the traditional data processing software. Big Data is governed by the three V’s which are Volume, Velocity, and Variety. The benefit of Big data over normal data is that big data makes it possible for us to gain more complete answers because we have more complete information collection and more of these complete answers means more confidence in our data which leads to a completely different approach to tackle different problems. Artificial Intelligence is a branch of computer science which deals with the building of smart machines that are capable of performing tasks that generally require human intelligence. In other words, AI is “ the study of agents that receive precepts from the environment and perform actions.”
The agricultural industry is one of the backbones of the Indian economy representing 17% of its GDP and more than 50% of its total workforce. Despite this, the sector faces numerous challenges that need to be addressed. The earliest revolution in agriculture in India was in the 1960s which was called the Green Revolution which was a period when Indian agriculture was converted into an industrial system due to the adoption of modern methods and technology. Now in the 21st century, when the world needs to produce 50% more food by 2050 with
a mere 4% increase in the cultivable land, AI and Big data volunteers as the promising solution for the world which must produce more food, using fewer resources.The application of science and technologies like big data and AI has given birth to what is known as Smart farming and Precision agriculture.
Precision agriculture is “a technology-enabled approach to farming management that observes, measures, and analyzes the needs of individual fields and crops.”And the development of precision agriculture is shaped by two main trends: advanced analytical capabilities and the use of Ariel imagery, sensors, accurate weather forecasts,or all together intelligent robotics.Smart Farming is the implementation of information and data techniques for optimizing complex farming systems. The focus of smart farming is on accessing the data and using that data in a smart way.

The collection of data can be done through the integration of AI with :

Sensors and RADARS: Sensors placed in fields allow farmers to obtain detailed maps of both the topography and resources in the area, as well as variables such as acidity and temperature of the soil.

Drones, Cameras, and Infrared: They can scan the soil for their nutritional properties, help understand the reactions of different seeds with different soils and different fertilizers, the impact of changing weather on the soil and the growth of the crops.

GPS receivers: Provides precise location information for soil and crop measurements to be mapped.

AI-aided machines for crop sowing: Crops can be sowed using AI-aided machines at equidistant intervals and appropriate depths with utmost precision using analytics to determine the best time to sow based upon climate data, market conditions for input and output, historical conditions
➔ Microsoft in collaboration with ICRISAT, developed an AI Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. The app sends sowing advisories to participating farmers on the optimal date to sow and all that the farmers need is a feature phone capable of receiving text messages. The advisories contained essential information and the increase in yield ranged from 10% to 30% across crops.

AI-enabled robots for harvesting: Approximately 40% of the annual agricultural costs go into the employment of labor, primarily for sowing and harvesting. Hence, AI-enabled robots for harvesting can lead to huge cost savings by reducing the need for approximately 4 agricultural laborers per acre of land. Additionally, at the time of harvest, crops can be sorted according to pre-identified grades at the time of harvest, saving time and enhancing the quality of crops.
Therefore, we see that AI used in analyzing and monitoring soil health, helps improve the sustainability of a given piece of land. It has the potential to increase the per acre crop output as well as decrease the input costs of farmers and save time for them. AI can find great application in precision weed and pest management.

All the information and the collected data are analyzed using big data analytics to come up with practical solutions with better results.

Some of the solutions that can be provided by the analysis of data are:

Manage revenue costs by decreasing crop failure’s probability.
Measure, store and dissect the data to enhance yield quality.
Enhance preventive care and increase producer-consumer satisfaction.
Real-time optimization of farming machinery.
● Cloud-hosted information resources for farmers.
● Automated irrigation recommendations based on accurate weather forecasts and real-time field data.
● The geological maps produced are of high determination recognizing the variety of soil dampness that would direct to the definitive utilization of the watering system.

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