Agriculture World

Solving Indian Farming Problems by Artificial Intelligence & Data Analytics

Vivek Verma
Vivek Verma
AI in agriculture
AI in agriculture

Artificial Intelligence and Data Analytics have seen rapid adoption in agriculture but majority of this has been in supply chain management & not necessarily in field farming techniques. This is due to the age old practices of agriculture which has typically been governed by intuition rather than hard facts. But now, this has started to change as farmers now are increasingly offered the opportunities to use intelligent farming practices with the help of new & emerging techniques.

Digital Transformations in Agriculture

These digital transformations are not only improving farm management but also other measures to enhance profitability & stability on the field. For instance, IBM has been computing actionable agronomic insights for farmers including pest-risk forecast & critical weather related information. Microsoft has also enabled unique data enabled farming techniques using Azure FarmBeats.

Farming depends on a lot of factors which are often out of our control such as weather, pests, market conditions among others, Artificial Intelligence driven Data Analytics have the potential to help farmers in avoiding expensive mishappenings that are generally results of inaccurate information. AI & Data Analytics can help farmers in becoming more efficient & cost effective.

In fact, according to a report, the worth of AI in agriculture is expected to reach 4 billion by 2026.

Let’s look at some of the ways in which AI & Data Analytics can play crucial role in tackling farmer’s problems.

1. Data Driven Farm Management:

AI & other emerging technologies can automate some farm activities enabling cost reductions & improved decision making. Not only do they eliminate the need for superfluous agronomists & advisors, it greatly helps farmers run critical farm management practices. However, emerging tech comes with a lot of promises when it comes to pruning in modern agriculture. The sector arguably suffers from data issues at a very basic & granule level.

2. Integrating Image Based Insights:

Adoption of tools to track & collect data especially drone based images from farms for lending support to critical decision making have accelerated precision agriculture. Tech driven methods to monitor crops and scan fields have helped farmers in various ways like making crucial weather predations, draw early detection systems for diseases & more. For instance, European company Aerialtronics uses drones, AI, IoT& computer vision technology to provide image analysis in real time. This can be used by the farmersfor disease detection by preprocessing images of leaves, crop-readiness identification & field management especially when it comes to resource allocation.

Another company Blue River Technology uses computer vision & AI to build a smart machine that is designed to detect, identify & make critical decisions around each & every plant in a field.

3. Tackling Financial challenges in Agriculture:

According to CEO of SourceTrace, VenkatMaroju, better data will help agricultural players become more future ready. There are number of other ways in which technology can be leveraged to ease the financial stress on farmers. As per a report, IBM has been developing a unique solution driven by Artificial Intelligence to tackle crop insurance & credit challenges. Cognitive credit farm scoring applications that determine how much yield a farmer is likely to make are being employed to make accurate estimates of their credit worthiness.

Although AI & Data Analytics have not yet been widely adopted in agriculture, especially in India, it holds tremendous potentials to give farmers & their products a brand that can command better prices.

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