The problem AgriCol solves

The global population is expected to reach 10 billion people by 2050,which means double agricultural production in order to meet food demands which is about 70% increase in food production.Farm enterprises require new innovative technologies to face and overcome these challenges.AI helps to resolve these problems.
Agricultural systems pose many challenges and problems that can be formulated as optimization problems.Among these one of the major challenge is crop selection. That is, to decide the proper set of crops to be cultivated with proper irrigation scheme. Such decisions are made for the maximization of net profit and minimization of crop wastage.

Proposed Methodology:
AgriCol is an AI based guiding application that provides a complete crop plan to farmers(sowing to harvesting) using soil data(soil type,pH) and environmental factors such as temperature, humidity,etc.. The crop plan consists of:
Success rate of crops at particular season
Days of germination and maturation
Nutrients to be supplied
Irrigation and pest prevention information
Accuracy of AgriCol is more than 90%.Farmers can get all details about the crop in a single application and finally increases yield.

Data Used:
For this project, we have collected some of the data from farmers such as pH,N,P,K values,soil type, water availability, irrigation details,location details. We also use rainfall,temperature,humidity data from farmers.

Backend Model:
The backend model consists of three different models combined together to increase the accuracy.Initially using the NPK and pH values the model provides an output with approximately 10 values.This along with soil type and water availability in the second model alters the crop with maximum probability of success. To improve the accuracy further we have also passed the output with crop yield data to get high accuracy.

Challenges we ran into

As it consists of a large tech stack, we found it difficult to get project done in short span of time. It took time for us to learn the stuffs.Further we have challenges in connecting python flask and reactjs. It took some time to learn and connect it.We found some difficulty in connecting third party API along with our code.

Overall it was a nice learning experience through HACK AT SITS. Thanks for the opportunity.

What Next for Agricol???
The development phase is almost done. We have planned to take this furthermore. So testing is to be done with some of the manual and practical data to test the accuracy. Next we must get clarity with farmers for our project. Mostly we also need regional language so that farmers can use our application wisely. In the deployment phase we will be using AWS to host our product to the outer world!!!!