Inspiration
After interacting with women farmers we figured out they face many problems in their daily routine in the farm. As many tasks in the farm need vehicles and those vehicles are massive in size and weight and women face problems while using them especially in fruit farming. They didn't had access to proper resources and technologies for reducing the manual labour. This would help women and elderly farmers to overcome these challenges and improve their livelihoods as it is cheap, portable, easy to use and increases mobility.
What it does
The smart, affordable, and accessible agricultural solution for women farmers is a solar+ electric powered multipurpose vehicle that addresses a variety of challenges faced by farmers, including grass cutting, irrigation, pesticide application, seed sowing, weather forecasting, soil testing, pesticide level indication and onion cutting. The vehicle is equipped with a variety of IoT sensors and actuators that allow it to perform these tasks autonomously or with minimal human intervention. As it is also powered by solar energy, it is affordable and sustainable for women farmers. It can be used to increase the mobility and reduce agricultural losses.
How we built it
We conducted research using approaches like newspaper surveys, literature surveys, and government surveys after considering all the challenges faced by women farmers into account. We made an effort to address such issues with our creative solutions. When we first started designing the vehicle, our goal was to make it lightweight so that women farmers could use it with ease. The process of choosing components and assembly followed. The software was created afterwards and included into the app. The vehicle's batteries are manufactured and put assembled in our workspace. For BMS, there is a sophisticated app. We also paid attention to the vehicle's mobility. This vehicle comprises the portable weather and soil boxes. Those are designed in a manner that makes it portable, cheap and handy. Those are detachable.
Challenges we ran into
Our team is formed up entirely of female students studying computer engineering. It was challenging for us because we have a background in computers and our idea is mechanical based. For this project, we requested a team of professionals. We worked on Embedded Systems, IoT, ML, Gene AI, Designing and BMS for this project, all of which were challenging for us.
Accomplishments that we're proud of
We developed a solution that addresses a variety of challenges faced by women farmers. We are all women in our team, and we know the challenges that women farmers face. That's why we designed this solution specifically for them. This project has the potential to make a real difference in the lives of women farmers around the world. Our solution is a way to empower them. We are grateful for the opportunity to work on this project and help women farmers and we are excited to see what the future holds for women farmers around the world.
What we learned
Technology can be used to empower women farmers and make agriculture more efficient and sustainable. We engaged with women farmers throughout the design and implementation process. We learnt many things related to technology like the mechanical parts, IoT, ML, Gene AI and also we learnt teamwork. And the best thing about this project is "For a woman, from a woman".
What's next for Krushi Mitra: Smart solution for women farmers
To ensure the success of Krushi Mitra, we intend to broaden its audience, create additional features and functionality, provide training and support to women farmers, and collaborate with other organisations.
Built With
- 3d-model
- amazon-web-services
- android-studio
- app
- ar/vr
- arduino
- azure
- batteries
- blender
- c++
- django
- electric-motors-and-controllers
- firebase
- flask
- generative-ai
- google-cloud
- googlecloudstorage
- gripper
- image-recognition
- iot-sensors
- java
- javascript
- laravel
- li-ion
- machine-learning
- mongodb
- mysql
- python
- pytorch
- raspberry-pi
- robotic-arm
- scikit-learn
- solar
- spring
- tensorflow
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