Depp Learning Tomato Leaf Disease Prediction project deployment using flask, Keras, TensorFlow, sklearn libraries.

A simple CNN model to detect and classify ten different types of tomato leaf disease.

Prediction of tomato leaf diseases using Le-Net

TomatoLeafAI is a deep learning-based plant disease detection system built using TensorFlow and Flask. It classifies tomato leaf images into Early Blight, Late Blight, or Healthy using a custom-trained CNN model.

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Here I am combining best of the both worlds, one is traditional Machine Learning and Deep Learning to create a hybrid model which classifies Tomato Leaf Diseases.

Detector and tracker for leaf disease detection and tomato counting

Transfer Learning by VGG16, VGG19 and InceptionV3

LeafDisease-AI is the first framework for cross-domain tomato leaf disease detection, bridging the gap between laboratory research and real-world agricultural deployment. This repository implements a unified optimization approach integrating ensemble learning, knowledge distillation, and quantization for edge-compatible disease detection.

基于 Tomato-Leaf-Disease-Detection 数据集和 YOLOv11 实现番茄病检测

Identification of diseases from the images of a tomato leaf is one of the interesting research areas in the agriculture field, for which machine learning concepts of computer field can be applied. My research presents a prototype system for detection and classification of tomato leaf diseases based on the images of infected tomato leaf. We consider 10 tomato diseases named Bacterial_spot, Early_blight, late_blight, Leaf_Mold, Septoria_leaf_spot, Spider_mites Two-spotted_spider_mite, Target_Spot, Tomato_Yellow_Leaf_Curl_Virus, Tomato_mosaic_virus, healthy. It can also detect Healthy leafs. In this research, I used deep learning based model (CNN) for classification. First, I pre-processed the image dataset very carefully because preprocessing is the most important part of this research. Then I trained my model and validate according to the dataset. I test various techniques for this research but faster rcnn works pretty well for my dataset, it gives an accuracy level of 89%. If there is no image of the tomato leaf then it can also be detected.

Tomato Leaf Disease Detection System

This repository utilizes TensorFlow Object Detection for tomato leaf disease identification, including setup scripts, dataset preparation, model training, TensorFlow Lite conversion, and inference tools. It serves as a guide for efficient disease detection in agriculture.

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The Tomato Leaf Disease Detection model is CNN-based technology for classifying ten infections from images of tomato leaf.

An intelligent AIoT system for plant health monitoring. Leverages ESP32 for environmental data collection, FastAPI & YOLO/CNN for tomato leaf disease detection, and a React-based Web Dashboard for real-time tracking.

This dataset contains labeled images of tomato plant leaves affected by various diseases, including early blight, late blight, and leaf mold, as well as healthy leaves. It is intended for use in training and evaluating machine learning models for plant disease detection and classification.

Using state of the art deep Convolutional Neural Networks to classify leaf images.

Tomato Leaf Disease Detection Using CNN is a machine learning project that identify and classify various diseases affecting tomato plants using Convolutional Neural Networks (CNN).

Tomato leaf disease detection using CNN

Tomato Leaf Disease Detection Using Machine Learning

Tomato Leaf Disease Detection Using Convolutional Neural Networks is a sophisticated approach to aid in the early diagnosis of diseases affecting tomato plants. Leveraging the power of deep learning, particularly convolutional neural networks (CNNs).

A school project of utilizing YOLOv5 Object Detection algorithm to train a pre-trained model with and test it against a dataset containing more than 6000 tomato leaves of 5 classes: Healthy, Bacterial Spot, Early Blight, Late Blight, Powdery Mildew.

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The project "Detection of Tomato Leaf Diseases Using CNN" is a computer vision application that utilizes Convolutional Neural Networks (CNN) to identify and classify diseases affecting tomato plants based on images of their leaves.

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AI-powered pest detection application using YOLOv11 trained with a large tomato leaf diseases dataset

Detection of Potato & Tomato plant healthiness by rectifying the plant's diseased & healthy leaf images using CNN and Mask RCNN approach.