Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.

The project is based on the leaf disease detection using cnn model and provide remedies for the disease plants.

Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

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.

Plant Disease Detection Using CNN Deep Learning Project A deep learning–based system that automatically detects diseases in plant leaves using image classification. This project uses a Convolutional Neural Network to classify leaf images into healthy or diseased categories, helping farmers and researchers identify crop diseases at an early stage.

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This project aims to develop a CNN-based system for early detection and classification of plant diseases using leaf images to support precision agriculture.

MATLAB-based solution for rice disease classification using CNNs. Enables efficient data processing, model training, and evaluation for diagnosing bacterial leaf blight, leaf smut, and brown spot diseases in rice crops. Streamlined interface facilitates easy deployment and customization for accurate agricultural disease detection.

This project uses CNNs for early detection and classification of rice leaf diseases. It analyzes leaf images to identify diseases and recommends eco-friendly pesticides for sustainable crop management. The solution aids in proactive disease management, reducing pesticide use, and promoting environmental safety.

Developed a Machine Learning-based Android app for soybean disease detection. Trained CNN on leaf images and deployed using ONNX Runtime for real-time classification via camera/gallery. Provides farmers with instant, offline, and low-cost disease diagnosis to support smart farming.

The main aim of this project is to identify the diseases a plant leaf is suffering from so that we can give clear instruction to the farmer about the disease and the measures to be taken using CNN thereby reducing the economical losses. The Plant diseases effect the growth of the crop and reduces the quality of production. Convolutional neural networks helps in identification of features from the input images without the intervention of humans. Convolutional neural networks contains different layers and in each layer there are different activation function called neurons and have an impact on input image at each layer for the feature identification and disease detection. Based on the disease certain prevention measures are insisted to the farmers. Neural networks are used because of their great impact in the image classification.

The project evaluates different CNN models like Squeezenet and Googlenet

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Convolutional NeConvolutional Neural Network (CNN) | Feature Learning | Streamlit ->In-order to learn and classify the graph leaf CNN model is built using TensorFlow and Keras with accuracy of 92.3% ->Built a Sequential model with three convolutional layers for feature extraction and dense laye

Plant leaf disease classification and detection using deep learning and CNN.

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This is a maize leaf disease detection and classification android application built using java and a deep learning model (CNN)

Image-based Plant Disease Detection web application using CNN Deep Learning model. Detects diseases from plant leaf images with 38 disease classifications. Built with Python, TensorFlow, Keras and Streamlit.

Deep Learning-based system for mango leaf disease detection using Python, OpenCV, and TensorFlow. Uses CNN and MobileNetV2 for image classification and integrates OpenWeatherMap API for weather-based disease risk alerts and treatment suggestions.

A full-stack Potato Disease Detection System using Convolutional Neural Networks (CNN) for classification, FastAPI for backend API, and ReactJS for the user-friendly frontend. Upload leaf images and detect common potato diseases in real time.

Cocoa Disease Detection is a machine learning project that identifies diseases in cocoa plant leaves using image classification. It uses a CNN model trained on leaf images to classify them as healthy or diseased. Built with Python, TensorFlow, and OpenCV, it helps in early detection for better crop management.

Automated detection and multi-class classification of tobacco leaf diseases using CNN with transfer learning (MobileNet/EfficientNet). Includes dataset preparation, preprocessing, model training, evaluation, Grad-CAM visualization, and an optional Streamlit web app for real-time predictions.

Potato Disease Detection using FastAPI & TensorFlow This project is a deep learning-based web application for detecting potato leaf diseases (Early Blight, Late Blight, and Healthy leaves). It utilizes a Convolutional Neural Network (CNN) model built with TensorFlow/Keras and is deployed using FastAPI for real-time image classification.

1. To develop an Automated Classification System based on Convolutional Neural Networks (CNN). 2. To enhance Early Detection and Diagnosis of tomato leaf diseases to support timely intervention and reduce crop losses. 3. To evaluate Model Performance Using Real-World Data on the CNN model through comprehensive testing on a separate dataset.

AI-based system for detecting cancer and plant leaf diseases using CNNs. This project includes image classification models, trained on medical and agricultural datasets, integrated into a web app with React (Frontend) and Flask (Backend) for real-time detection. Enhancing early diagnosis in healthcare and agriculture.

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