Tomato Architecture - A common sense driven approach to software architecture

Tomato Firmware for the x86_64 and arm64 architectures

A sample Spring Boot application following Tomato Architecture

Tomato Architecture - A pragmatic approach to software development

Official Implementation of paper Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

URL Shortener Application following Tomato Architecture

A BookStore application using Spring Boot following Tomato Architecture

This is the code to compute persistent homology to measure tomato leaf shape, leaf serrations, and root architecture

CPU-Constrained Evaluation of Modern Hybrid Transformers and CNN Architectures for Tomato Leaf Disease Classification Using the PlantVillage Dataset 🌱

Comprehensive benchmarking suite for tomato leaf disease classification, evaluating 23 classical machine learning and modern deep learning architectures.

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Tomato Classification Project 🌱🍅 This project focuses on building a machine learning model to classify different types or conditions of tomatoes. The system leverages a CNN-based architecture for image recognition and classification. It is designed to assist in identifying tomato varieties, detecting ripeness levels.

Here I have created a deep convolutional neural network architecture that correctly identifies the nine harmful tomato diseases with an accuracy of 94 percent.

Evaluation of traditional, modern, and hybrid CNN architectures: ResNet-50, ConvNeXt-Tiny, and FastViT-T8 for tomato leaf disease classification under CPU-only constraints 🌱

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Enhanced lightweight YOLOv8 architecture for tomato maturity levels recognition.

Personal Project utilizing CNN-LSTM hybrid architecture to classify text data from Rotten Tomatoes

This project used CNN architecture to create a multi-class image classification model for a Kaggle competition with tomato images. My architecture achieved 100% accuracy.

Aims to predict diseases in tomato plants based on images of their leaves using the VGG16 and CNN (Convolutional Neural Network) architectures.

A game using the ‘Tomato’ API - https://marcconrad.com/uob/tomato/doc.php This assignment asks to reflect on various concepts, paradigms and architectures related to Software Development. Software design principles (low coupling and high cohesion), event-driven programming, interoperability, and virtual identity.

Tomato disease development is favoured by long periods of moderate temperatures, dew, and high humidity. Early detection of the disease may help save the product from severe infection.After Training using the VGG16 Architecture we were able to achieve 91.38% accuracy in properly predicting the disease related to tomatoes

Tomato OS is a lightweight, Linux-based operating system designed specifically for headphones. It provides a user-friendly interface and a range of applications to enhance the listening experience. Tomato OS is built on top of the Linux kernel and provides a modular architecture that allows for easy customization and development.

Analyze and compare the convolution neural network models like VGG architecture (built from scratch), VGG architecture (using pre-trained weights), GoogLeNet, AlexNet to detect the of tomato plant diseases using leaves images. Image preprocessing is used to convert and resize the input dataset.

Convolutional Neural Network Based Deep Learning Architecture Implemented to Classify 10 Disease classes among Tomato leaves. The Model was created and trained using Python, Tensorflow, Google Colab and Colab's built in GPU.

🍅 Tomato architecture template is a new design pattern for resilient microservices. It's perfect to build and maintain complex systems!

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Tomato Timer Minimal UI Version - MVC Architecture

Tomato leaf disease detection with Transfer learning architecture

EfficientNetB5 architecture for tomato diseases detection integrated into Tello Drone

A Tomato Farm based on Immutable Screaming Architecture in Golang