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InsInspiration

Arecanut farming is an important agricultural activity in Karnataka, and farmers face challenges in identifying crop diseases early, managing irrigation, and responding to changing weather conditions. We were inspired to use Artificial Intelligence and smart technology to help farmers make better decisions and manage their orchards more efficiently.

What it does

Agri AI – Arecanut Guardian is an AI-powered orchard management system designed to support arecanut farmers. It aims to analyze arecanut leaf images for potential diseases, monitor environmental conditions such as temperature, humidity, soil moisture, and rainfall, and provide crop-risk alerts and irrigation recommendations. The system presents important information through a simple dashboard with Kannada and English language support.

How we built it

The project uses Python for the application logic, Flask for the web application, TensorFlow/Keras for AI-based image analysis, and HTML, CSS, and JavaScript for the user interface. The proposed system combines leaf-image predictions with orchard and weather information to generate useful insights. A database can be used to store orchard readings and related information.

Challenges we ran into

One of the main challenges is integrating AI-based leaf analysis with environmental information in a single system. Other challenges include obtaining reliable agricultural data, presenting risk information in an understandable way, and designing a simple interface for farmers. We aim to address these challenges through modular development, testing, and clear recommendations.

Accomplishments that we're proud of

We are proud to develop an agriculture-focused AI solution that addresses real-world farming challenges. The project brings together crop-health analysis, environmental monitoring, and orchard management in one platform, with a focus on accessibility and practical use for local farmers.

What we learned

Through this project, we are learning how AI, web development, environmental data, and database technologies can work together to solve agricultural problems. We also learned the importance of designing technology around users' needs and making AI-generated information understandable and useful.

What's next for Agri AI – Arecanut Guardian

Our next steps are to improve disease detection, integrate reliable weather data and real-time sensors, strengthen crop-risk assessment, and provide more personalized recommendations. We also aim to test the system with real orchard data and expand its usefulness for arecanut farmers through a bilingual, farmer-friendly interface.piration