How we helped our client to create a crop hedging tool using AI & Machine Learning?
GoFarmYourself was set to work on a combination of machine learning and the latest software technologies to analyze the market and make accurate predictions using the data provided at the end backend, needing expertise in both fields.
Technology
Python, React, Node JS. Further, S3 buckets, stripe API, and AWS API Gateway/Lambda.
Industry
Crop hedging tool
Accomplishments
About the project
25% of businesses plan to use ML for security purposes (Statista, 2019)
Go Farm Yourself is a crop hedging tool for farmers that predicts the market value of crops and uses the stoplight feature to indicate the ideal time to sell its produce. It analyzes the prices of three crops - wheat, corn, and soybean to help the farmers make the most of their produce.
This tool uses AI and Machine learning models to predict the right time for the farmers. It works by tracking the price and the market momentum to recommend you the right time to sell the crops. Additionally, the interface has been built keeping the farmers in mind and is very simple to operate - the GFY indicator uses three lights to indicate if you need to wait, pause for a while, or immediately sell your crops.
Our team intricately worked on its front and backend development to achieve the desired functions, discussed in the sections below.
NDP Analysis
Our team understood the need of the client to develop a tool that would target the farmers and analyze the market trends of the three crops accurately,
So although it needed the use of highly advanced technologies like machine learning and Python, the front end had to be simple to make it easier to use. So we made it simple by using three indications - red, yellow, and green. It also needed the right risk and predictive analysis tools. Further to save the hassle it uses a subscription model which has been made easy with the use of AWS API Gateway/Lambda.
The Challenge
GoFarmYourself was set to work on a combination of machine learning and the latest software technologies to analyze the market and make accurate predictions using the data provided at the end backend, needing expertise in both fields.
The spotlight needed intricate design as it would display accurate results using three colors - red, yellow, and green, making understanding this easier for the users. Further, it works on a subscription model necessitating payment gateway integration.
These functionalities would rely on a machine-learning algorithm to make historical data available to the users.
Implementation
Gofarmyourself is an easy-to-use website with a simple interface. The technologies it relies upon are stable and deliver high performance, making it more efficient and accurate. Our team was constantly engaged in discussions with the client and brainstorming sessions to pick up the perfect technologies that helped us handpick the perfect combination for them.
Backend
GFY relies on high-end technologies known for their performance. It has Python for the backend, and React for the front end while some features use Node JS. Further, it also uses S3 buckets, stripe API, and AWS API Gateway/Lambda.
It works on a simple machine learning model where data is processed and added as CSV files to Amazon Web Services. React in the front end, then read it and make it legible.
Front end
The use of React in the front end has made GFY even more competent. It uses the spotlight method to display the results to the users. Since they are just like traffic signs, they are easy to understand.
- Red indicates that you are at risk and must immediately sell the crops
- Yellow signifies favorable conditions where you should start preparing to sell.
- Green, on the other hand, implies things are fine and you can relax.
The historical data is available to the users and presented in the form of lines with similar colors for simplicity - red lines show risk areas, yellow intermediate, and green range.
Subscription mode
Go farm yourself works on a subscription model where users sign up for a monthly or yearly subscription to use their services. The features of this trade application are accessible to the users after signing up which called for the use of payment gateway integration. Our team accomplished this task using Stripe API and AWS API Gateway/Lambda functions.
Industry Impact
40% - The estimated productivity improvement from AI use (Accenture)
When we started working on Go Farm Yourself, it was a short-time contract. However, they were thrilled by our work and it lead to six-month partnership. This tool rightly helps the farmers to make maximum benefit from their crops and also saves crop wastage. Consequently, Go Farm Yourself turned into a long-term collaboration where our team also assisted them with launching their website, which shares a substantial share of their profits.
NDP Analysis
Our team understood the need of the client to develop a tool that would target the farmers and analyze the market trends of the three crops accurately,
So although it needed the use of highly advanced technologies like machine learning and Python, the front end had to be simple to make it easier to use. So we made it simple by using three indications - red, yellow, and green. It also needed the right risk and predictive analysis tools. Further to save the hassle it uses a subscription model which has been made easy with the use of AWS API Gateway/Lambda.
The Challenge
GoFarmYourself was set to work on a combination of machine learning and the latest software technologies to analyze the market and make accurate predictions using the data provided at the end backend, needing expertise in both fields.
The spotlight needed intricate design as it would display accurate results using three colors - red, yellow, and green, making understanding this easier for the users. Further, it works on a subscription model necessitating payment gateway integration.
These functionalities would rely on a machine-learning algorithm to make historical data available to the users.
Implementation
Gofarmyourself is an easy-to-use website with a simple interface. The technologies it relies upon are stable and deliver high performance, making it more efficient and accurate. Our team was constantly engaged in discussions with the client and brainstorming sessions to pick up the perfect technologies that helped us handpick the perfect combination for them.
Backend
GFY relies on high-end technologies known for their performance. It has Python for the backend, and React for the front end while some features use Node JS. Further, it also uses S3 buckets, stripe API, and AWS API Gateway/Lambda.
It works on a simple machine learning model where data is processed and added as CSV files to Amazon Web Services. React in the front end, then read it and make it legible.
Front end
The use of React in the front end has made GFY even more competent. It uses the spotlight method to display the results to the users. Since they are just like traffic signs, they are easy to understand.
- Red indicates that you are at risk and must immediately sell the crops
- Yellow signifies favorable conditions where you should start preparing to sell.
- Green, on the other hand, implies things are fine and you can relax.
The historical data is available to the users and presented in the form of lines with similar colors for simplicity - red lines show risk areas, yellow intermediate, and green range.
Subscription mode
Go farm yourself works on a subscription model where users sign up for a monthly or yearly subscription to use their services. The features of this trade application are accessible to the users after signing up which called for the use of payment gateway integration. Our team accomplished this task using Stripe API and AWS API Gateway/Lambda functions.