Add project: Crop-Disease-Classifier

Author: github-actions[bot]

submissions.json

@@ -53,6 +53,18 @@
     "repository_url": "https://github.com/abhay-7-7-7/ResqRoute.git",
     "description": "Introduction Every second counts in a medical emergency. In heavy traffic, ambulances struggle to reach hospitals on time, costing precious lives. Our integrated system combines AI-powered traffic management with Digital Twin technology inside the ambulance to ensure seamless emergency response. Problem Statement\n\nTraffic congestion causes significant delays in emergency response times.\nLack of real-time traffic management leads to inefficiencies in clearing paths for ambulances.\nManual intervention by traffic police is often slow and ineffective.\nMedical professionals in hospitals lack real-time patient data before arrival.\nCritical treatments are delayed due to lack of immediate health insights.\nDoctors often have to make instant decisions with limited patient data, increasing the risk of errors. Proposed Solution: AI-Powered Emergency System Our AI-driven system integrates two key technologies:\nResQRoute: AI-based traffic management that detects ambulances and clears traffic automatically.\nDigital Twin for Health: Inside the ambulance, AI builds a real-time patient clone using medical data to predict their health status and guide doctors before arrival.\nDoctor Preparation System: AI provides hospitals with real-time patient insights, ensuring the medical team is prepared for immediate treatment upon arrival. How It Works\nAmbulance Detection: AI cameras recognize ambulance symbols and prioritize traffic light changes.\nReal-Time Traffic Adjustment: AI controls traffic signals dynamically to create an open path.\nPatient Cloning Inside Ambulance: Medical devices inside the ambulance monitor vital signs and feed data to an AI model to simulate future health conditions.\nDoctor & Hospital Preparation:\nAI provides a real-time patient health clone to doctors.\nDoctors analyze vital trends and predict possible complications before arrival.\nAI suggests probable treatments based on patient history and symptoms.\nHospital staff prepares the necessary operation theaters, ICUs, or emergency rooms based on AI risk assessment.\nOptimized Emergency Care: Predictive analytics help doctors make faster, more accurate decisions for treatment.\n\n\nHere is the website like we deployed using streamlit: https://resqroute.streamlit.app/"
   },
+  {
+    "name": "ABHAY PARAMESWER R",
+    "project_name": "ResQRoute",
+    "repository_url": "https://github.com/abhay-7-7-7/ResqRoute.git",
+    "description": "Introduction Every second counts in a medical emergency. In heavy traffic, ambulances struggle to reach hospitals on time, costing precious lives. Our integrated system combines AI-powered traffic management with Digital Twin technology inside the ambulance to ensure seamless emergency response. Problem Statement\n\nTraffic congestion causes significant delays in emergency response times.\nLack of real-time traffic management leads to inefficiencies in clearing paths for ambulances.\nManual intervention by traffic police is often slow and ineffective.\nMedical professionals in hospitals lack real-time patient data before arrival.\nCritical treatments are delayed due to lack of immediate health insights.\nDoctors often have to make instant decisions with limited patient data, increasing the risk of errors. Proposed Solution: AI-Powered Emergency System Our AI-driven system integrates two key technologies:\nResQRoute: AI-based traffic management that detects ambulances and clears traffic automatically.\nDigital Twin for Health: Inside the ambulance, AI builds a real-time patient clone using medical data to predict their health status and guide doctors before arrival.\nDoctor Preparation System: AI provides hospitals with real-time patient insights, ensuring the medical team is prepared for immediate treatment upon arrival. How It Works\nAmbulance Detection: AI cameras recognize ambulance symbols and prioritize traffic light changes.\nReal-Time Traffic Adjustment: AI controls traffic signals dynamically to create an open path.\nPatient Cloning Inside Ambulance: Medical devices inside the ambulance monitor vital signs and feed data to an AI model to simulate future health conditions.\nDoctor & Hospital Preparation:\nAI provides a real-time patient health clone to doctors.\nDoctors analyze vital trends and predict possible complications before arrival.\nAI suggests probable treatments based on patient history and symptoms.\nHospital staff prepares the necessary operation theaters, ICUs, or emergency rooms based on AI risk assessment.\nOptimized Emergency Care: Predictive analytics help doctors make faster, more accurate decisions for treatment.\n\n\nHere is the website like we deployed using streamlit: https://resqroute.streamlit.app/"
+  },
+  {
+    "name": "Sacheth Sivaprasad",
+    "project_name": "Crop-Disease-Classifier",
+    "repository_url": "https://github.com/sachethsivaprasad/Crop-Disease-Classifier",
+    "description": "Crop Disease Classifier is a web-based application that helps farmers and agricultural researchers identify plant diseases using machine learning. The system allows users to upload images of crop leaves, which are analyzed by a pre-trained AI model for plant disease identification. The model predicts the disease along with confidence scores, enabling users to take timely action. Built with Flask for the backend and Hugging Face Transformers for image classification, the project aims to provide an accessible and efficient solution for early disease detection in crops."
+  },
   {
     "name": "Ram Madhav",
     "project_name": "Blood result analyzer",