The problem KisaanSathi solves

1.⁠ ⁠Problem Statement:

• Farmers often struggle to optimize crop selection for their specific conditions, leading to suboptimal yields and profitability. Lack of personalized recommendations based on factors like soil type and climate hinders efficient decision-making.

• Solution:

  • Our Smart Crop Recommendations system addresses this by providing personalized crop advice, leveraging data on soil type, climate, and other factors. Farmers can make informed decisions to maximize yield and profitability, optimizing their crop choices.

 2.⁠ ⁠Problem Statement:

• Identifying and managing plant diseases can be challenging, leading to crop losses. Farmers may lack the expertise to promptly detect diseases and apply effective solutions, impacting overall farm health.

• Solution:

  • Our Plant Disease Detection and Solution system solves this issue by offering real-time disease identification. Farmers can capture photos of affected plants, and our advanced system analyzes them, providing instant solutions to combat diseases and ensure crop well-being.

 3.⁠ ⁠Problem Statement:

• Selling crops efficiently and getting fair market value can be challenging for farmers. Lack of a transparent and competitive marketplace often results in suboptimal sales and profits.

• Solution:

  • Introducing Auction Functionality for Selling Crops, our platform provides farmers with a transparent marketplace. Farmers can showcase their harvest, allowing a wide audience of buyers to bid for the best-quality produce. This ensures fair value and efficient sales, benefiting both farmers and buyers.

Challenges we ran into

Challenges Encountered and Solutions

1. Overfittoptimized in Machine Learning Model:

• Challenge: During the development of our machine learning model, we faced overfitting issues where the model performed well on training data but poorly on unseen data.
• Solution: Implemented regularization techniques such as dropout layers and L2 regularization. Tuned hyperparameters and used data augmentation to increase the diversity of the training set.

2. Connection with Cloudinary for Photo Uploads:

   • Challenge: Integrating Cloudinary for photo uploads posed challenges in establishing a connection and handling image uploads reliably.
   • Solution: Checked Cloudinary API documentation thoroughly, ensured correct API key and secret configuration. Implemented error handling to address connectivity issues. Utilized

     cloudinary

     library for streamlined integration.

3. Implementing Socket.IO for Real-Time Auction Synchronization:**

   • Challenge: Implementing real-time bid synchronization using Socket.IO was complex, requiring bid updates to be instantly reflected across all connected clients.
   • Solution: Set up a Socket.IO server, established bid events and listeners. Ensured proper handling of bid updates on both the server and client sides. Debugged and optimized to prevent data inconsistencies.