Community Blog KrishAI: Water Usage Optimization for Efficient Farming

KrishAI: Water Usage Optimization for Efficient Farming

This project is from the team Tech Phantoms, which was awarded with the Innovation Award in the Global AI Innovation Challenge 2021 - Intelligent Weather Forecast for Better life.

This project is from the team Tech Phantoms, which was awarded with the Innovation Award in the Global AI Innovation Challenge 2021 - Intelligent Weather Forecast for Better life.

Project Introduction

Water Conservation helps large scale farmers in reducing their carbon footprint and water wastage, thus saving them from avoidable carbon taxation as well as ire from the media and activists over their incessant rates of water wastage to maintain their high yield fields via inefficient methods like flooding which consumes far more water than required.

Our product assists farmers in gauging the soil and water quality of their resources while supplementing them with more information about the possibility of rains, thus giving them an accurate representation and projection of their yield and how they need to go about their crops, thus saving loads of money that gets usually spent on drawing needless amounts of groundwater and time that gets spent on drawing up plans to save their yield from unforeseeable circumstances.

And if this project can get upscale, then we can also assist farmers in assessing their possible income based on the quality of their current yield and projection of future possibilities based on market powers using algorithms that take into consideration the current prices of the farmers' crops at the farmer markets.

In short, we dramatically allow farmers in engaging with better price discovery, better crop patterns, and better yields.



Solution and Project Value

Project Value

We are proposing a radical idea that can in long term optimize our water usage in the global agriculture space. Essentially using the power of AI and modern technology to introduce and democratize "Efficient Farming" to the world.

The end users of this project would be small and large scale farmers, agriculturists and other personnel dependent on agriculture and horticulture. The user base in India is about 42.6% of the Indian population (as of 2019) and around 30% of the world. Success of this project could pave way for expansion into other industries.

Effect on end users and community:

  • Increases yield in places where water is scarce and helps to use the water sustainably in a locality as the water usage would be very planned.
  • Reduces expenses and probability of incurring losses during production of the crop.
  • Increases food security and helps to reach the demand in near future.
  • This project is scale-agnostic and can help folks from all walks of life.


We would be using AI systems trained to assist farmers. This leads to better accounting of resources, thus reducing levels of virtual water while recognizing various metrics like rainfall, pollution, population, crop diversification, soil profiling etc. Weather forecasting is at the core of this project as well as agricultural/economic well-being of the farmer.

So, instead of the current weather advisory system or a post-harvest feedback loop, our product offers predictive and spontaneous analysis to continuously monitor the progress of the farmers' crops based on weather, water, and soil analyses. This helps us establish an accurate success/failure rate of the farmer's yield while notifying them about their potential profits/losses in the market based on said progress of crops.

Our product provides the best tailor-made packages that are scale-agnostic to immediately benefit the agricultural sector while paving possibilities for expansion into other areas of the economy.

Technology Highlights

We integrated AI in all the phases.
We used ConvLSTM based radar reflectivity nowcasting, IoT based data collection in soil profiling and seamless app integration.

Alibaba Cloud Products Used

About the Developer

Our team comprises of some talented individuals from different backgrounds and universities in India. The team captain Nihal Puram a student of IIT Madras and a self-taught deep-learning engineer. Thirumalaivasan and Rizwan khan are also students of IIT Madras and Deep learning enthusiasts. Kavin Ramesh Pillai (IILM) is our team's lead social and economic analyst. Mihir Achyuta (VIT Vellore) is the systems and electronics engineer. Anish Poludasu (NITW) is our mechanical engineer. Yash renwa (VIT Vellore) is our Full-stack app developer. We really believe our project can change the industry in a big way.

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