Work Experience

Selected for KSCST

This project, Banana Plant Leaves Disease Detection with Suitable Pesticide Recommendation, leverages Python, AI models, and a user-friendly frontend interface to detect common banana plant leaf diseases from uploaded images. Using deep learning (CNN) techniques, the system accurately classifies diseases such as Black Sigatoka, Panama Wilt, and others. Once a disease is detected, it automatically recommends the most effective pesticide based on a curated agricultural dataset. The frontend, built with HTML, CSS, and JavaScript, allows users to upload leaf images, view real-time results, and receive actionable recommendations, providing an end-to-end smart agriculture solution for farmers and researchers learn more.

The Grocery Management System is a simple and responsive web application built using HTML, CSS, and JavaScript. It allows users to efficiently manage grocery items by adding, editing, deleting, and searching through a dynamic list. The system features a clean UI, interactive elements, and local storage support to retain data across sessions. Designed for ease of use, it helps users track item names, quantities, and prices, making daily grocery management more organized and streamlined without the need for a backend or database integration learn more.

The Attendance Tracking System (ATS) is a robust, full-stack web application designed to efficiently monitor and manage employee or student attendance records in real-time. Developed using Java as the core language and powered by the Spring Framework (Spring Boot, Spring MVC, Spring Data JPA), the system ensures high scalability, maintainability, and performance. The backend logic is orchestrated through Maven for dependency management and project building, while MySQL serves as a reliable, relational database to store all attendance-related data securely. The frontend interface is implemented using JSP (JavaServer Pages), HTML, CSS, and JavaScript to provide a clean, user-friendly experience for users learn more.

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