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Realizou-se o projeto no primeiro semestre de 2024 como requisito parcial na avaliação da disciplina “TinyML - Aprendizado de Máquina Aplicado para Dispositivos IoT Embarcados” do curso de Engenharia de Computação da Universidade Federal de Itajubá (UNIFEI - _campus_ Itajubá).\n\n## Arquivos e links\n* O arquivo [deteccao_de_doencas_em_folhas_de_tomate.pdf](./deteccao_de_doencas_em_folhas_de_tomate.pdf) consiste no relatório detalhado do projeto.\n* O arquivo [nano_ble33_sense_camera.ino](./nano_ble33_sense_camera.ino) consiste no código utilizado no Arduino para testes.\n* O arquivo [folhas_de_teste.pdf](./folhas_de_teste.pdf) consiste em fotografias de folhas utilizadas para testes.\n* A licença do repositório pode ser consultada em [LICENSE](./LICENSE).\n* O projeto completo na plataforma Edge Impulse pode ser consultado no link [Tomato_leaf_disease_detection](https://studio.edgeimpulse.com/public/439395/live).\n\n## Dataset\n* [Tomato leaf disease detection](https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf/data)\n\n## Plataformas de desenvolvimento\n* [Edge Impulse](https://edgeimpulse.com/)\n* [Arduino IDE](https://www.arduino.cc/en/software)\n\n## Hardware utilizado\n* [Arduino Nano 33 BLE Sense](https://docs.arduino.cc/resources/datasheets/ABX00031-datasheet.pdf)\n* [Câmera OmniVision OV7675](https://www.ovt.com/products/ov7675/)\n* Notebook com Windows 11 para desenvolvimento\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgui-mrtns%2Ftomato-leaf-disease-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgui-mrtns%2Ftomato-leaf-disease-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgui-mrtns%2Ftomato-leaf-disease-detection/lists"}