The problem CropFinder solves

Crop finder solves one of India's biggest hurdles i.e. which crop should be grown when and where. India is suitable for almost all the crop but at appropriate time and appropriate location. So according to the various scientific methods and research , we were able to find at which location you should grow which crops to gain the most profit. The climate , temperature and the location plays a very important role in the growth of the crops. One of the other problem that india faces is the irrigation problem here . So were able to find where the crops needing more irrigation and less irrigations. The farmer here are also not able to understand the demand and supply.So we also ensure what can help them to maximize the profits for the given climates . The design of the website is simple .You would only require to input the most basic information like your state and what climate is there. If provided more time, it can also be able to automate through the location which even make it easier for the farmers. It can also help in the further planning of the crops you need to grow next season and so on. India one of the biggest problem of growing the appropriate crops for the given time can be solved through this. Furthermore the research can be done more and the website could be made more precise and with the modern farming procedure planning can improve farming and the crops quantity a lot. The crops grown at right time and temperature are of high quality .

Challenges we ran into

One specific bug or hurdle I encountered while building the Crop Finder project without using APIs was related to data acquisition and synchronization. Since APIs were not utilized, they required manual collection and management of data from various sources.

The challenge was to gather updated and reliable information on weather conditions, soil characteristics, and crop databases without the convenience of automated API calls. This process was time-consuming and prone to human errors, potentially leading to outdated or inaccurate recommendations.

To overcome this hurdle, I implemented the following approach:

Comprehensive data research: We extensively searched for reliable weather data sources, soil information, and crop databases. This involved referring to reputable agricultural publications, scientific research papers, government reports, and agricultural extension services. By collecting data from credible sources, I aimed to ensure the accuracy and relevance of the information.

Data verification and validation: By implementing stringent data validation processes, We aimed to minimize errors and improve the reliability of the recommendations.

User feedback and validation: To further enhance the reliability of the Crop Finder recommendations, We actively sought feedback from users and agricultural experts. Their insights and observations helped identify any discrepancies or errors in the data. By incorporating user feedback, We could rectify issues and continuously improve the accuracy of the recommendations.