Agrisense - Plant Health Monitoring Application based on Multiclass Image Classification to Detect and Diagnose Diseases in Food Plants
Github: https://lnkd.in/gZ5sdGNH
🌱 Overview of my Project:
I would like to proudly share about our latest project, Agrisense. Agrisense is an innovative application designed to monitor plant health by utilizing multiclass image classification technology. The project aims to detect, diagnose, and provide solutions for diseases in tomato, cucumber, and potato plants.
💡 New Skill I Discovered:
During the Agrisense project, I found myself involved in developing image classification models of tomato, cucumber, and potato plants using Computer Vision approaches. Understanding the basic concepts of Computer Vision brings interesting challenges, providing effective solutions for image classification, and creativity in designing technology. This expertise opens up opportunities to consider different approaches in Computer Vision, expanding the understanding of tools and techniques to solve complex visual challenges.
🤝 Memorable Moment with My Team:
There is nothing more satisfying than collaborating with a dedicated and passionate team. One of the most memorable moments was when our team successfully completed the development phase of the EfficienNet model for the Agrisense project. Supporting each other, complementing each other, we jointly overcame obstacles and achieved important milestones in the project. This togetherness not only enriched the project, but also fostered strong relationships between us.
📑 A Sprinkle of Project Documentation:
With Agrisense, we hope to make a positive contribution in increasing the productivity of tomato, cucumber, and potato crops. Thanks to the amazing Mindwave team and all who have supported this journey. Let's create innovative solutions for future agricultural challenges together! 🌾✨
I would like to sincerely thank Startup Campus, Kampus Merdeka, MSIB Kampus Merdeka and Direktorat Jenderal Pendidikan Tinggi, Riset dan Teknologi Kemdikbudristek for their cooperation throughout the project. Without their valuable support, this project would not have been such a tremendous success.
We are grateful for the close collaboration and positive contributions that each party has made. May this project be the foundation for future collaborations that are more productive and bring sustainable benefits. Thank you very much.
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