Ansh Kakkar

Passionate engineer with recently obtained degree. Energetic individual with outstanding interpersonal, analytical and critical thinking skills. Poised to build valuable experience and ready to contribute to team success with administrative and project management support.

Crop leaf disease detection using Artificial intelligence and Neural Networks -

• Objective: Developed an AI-powered system to identify and classify diseases in crop leaves using deep learning techniques.
• Data Collection: Acquired and labeled a comprehensive dataset of crop leaf images, covering various diseases and healthy specimens.
• Technologies & Libraries: Utilized Python, TensorFlow, Keras, and OpenCV for image processing and model development.
• Model Development: Implemented Convolutional Neural Networks (CNNs) to create an accurate model for disease recognition.
• Training and Validation: Trained the CNN model on a large dataset, employing techniques such as data augmentation and cross-validation to enhance model performance.
• Coding: Developed scripts for data preprocessing, model training, evaluation, and deployment.
• Deployment: Deployed the model using Flask to create a user-friendly web application accessible to farmers and agricultural experts.
• Impact: Provided a scalable solution for real-time disease detection, facilitating timely intervention and improving crop health and yield.

 Automated Transport System for Engine Handling Using Autonomous mobile robots -

• Objective: Automated the manual handling process of engines from the assembly line to the paint shop using advanced robotics and ROS.
• Unloading: Utilized a 6-DOF robot for precise unloading of engines from the assembly line.
• Pallet Handling: Implemented an AMR pallet dispenser to place engines on autonomous mobile robots (AMRs).
• Transport: Deployed AMRs to transport engines on pallets to various areas, including testing, storage, and the paint shop.
• Trajectory Planning: Used ROS (Robot Operating System) to plan and control the trajectory of the AMRs, ensuring efficient and safe transport.
• Technologies & Libraries: Integrated ROS, Python, and robotic control libraries for seamless automation and coordination.
• Impact: Enhanced efficiency and safety in the engine handling process, reducing manual labor and improving overall productivity in the transport and manufacturing pipeline.