VideoDemo
Figma Link: link

India is an agricultural country, though the agro-industry has a total of 12-13% of the GDP share. More than 60% of rural India depends on agricultural yield growth and allied agro-industry products. Crop yield prediction is an essential task for the decision-makers at national and regional levels for rapid decision-making. An accurate crop yield prediction model can help farmers to decide on what to grow and when to grow. It will act as a medium to provide the farmers with efficient information required to get high yields and thus maximize profits which in turn will reduce the suicide rates and lessen difficulties.

Hence our team came up with Project ** Cropify: Crop yield diagnostic tool !**

• It is an important metric to understand because it helps us to understand food security.
• It has been demonstrated that an increase in crop yields significantly reduces poverty.
• Providing climate-smart agriculture to increase crop yield while facilitating the achievements of crop production in a safe environment will help to achieve the goal of the 2030 agenda of Sustainable Development of the United Nations in transforming our world formulated as end hunger, achieve food security, improve nutrition and promote sustainable development.
• Reduce farmers' suicide rates.

• Web application with integrated machine learning model developed in order to provide farmers an approximation on how much amount of crop will be produced depending upon the given input.
• This will help the farmers to know the crop yield in advance to plan and choose a crop that would give a better yield.
• The ML model aims to help farmers to cultivate proper crops for better yield production. To precisely predict the crop yield it analyzes factors like district (assuming same weather and soil parameters in a particular district), state season, and crop type. It can be achieved using supervised and unsupervised learning algorithms.
• We used a random forest machine learning algorithm. The chosen dataset was loaded and 75% of the dataset was used to train the model and 25% to test the model. The model achieved an accuracy of 89%.

• Figma: After brainstorming many ideas, we moved on to wireframing in Figma, starting with lo-fidelity and working together to create clickable interactions on the high fidelity prototype.
• Machine Learning: implemented Random Forest group Algorithm using python libraries: Pandas, Matplotlib, Numpy.
• Front-end development using React.js
• Material-UI and CSS: we worked on the UI/UX, layout, CSS, and design.

• We faced difficulty in testing the data sets.
• Debugging issues and dynamic routing in React.js.
• Firebase authentication

✅We are proud that we were able to address such an important issue and find a practical and inclusive solution to it. ✅Our teamwork and cooperative workflow helped us to build the project in its entirety. ✅We are proud to have completed the whole UI design, develop and publish a fully functional website and developed an ML model.

• The very first thing we all learned was teamwork. We thought about the various problems around us and came up with a solution that we could build in the given time.
• The second thing we could implement Machine Learning Algorithm to our project.
• We learned better tactics of collaboration and brainstorming. We also learned a lot of issues faced in agro-industry while researching, that we were unaware of.

• We want to build a mobile application since it is handier.
• We wish to integrate language translation tools so that farmers can use out application without any linguistics barriers.