Plant Disease Detection is one of the mind-boggling issues when we talk about using Technology in Agriculture. Although researches have been done to detect whether a plant is healthy or diseased using Deep Learning and with the help of Neural Network, new techniques are still being discovered. For Fewer Data Classical Machine Learning Models are said to outstand given the data is pre-processed well. On the same theory here is my approach for Detecting whether a plant leaf is healthy or unhealthy by utilizing the classical Machine Learning Models, Pre-processing the Image Data. The data was fed to 7 Machine Learning Models with 10 fold cross-validation out of which Random Forest Classifier outperformed all the other models giving an accuracy of 97% on the test set.

This repo contains the python codes of my final thesis "Analysis of leaf species and detection of diseases using image processing and machine learning methods".

Leaf Disease Detection using Image Processing and Deep Learning

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.

Crop-Disease-Detection-via-Image-Processing uses machine learning and image analysis to identify plant diseases from leaf images. The system preprocesses images, extracts features, and applies a trained model to classify diseases accurately.

Built a deep learning model using tensorflow and keras in python for grape leaf disease detection. Used OpenCV for image processing

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.

Paddy Leaf Disease Detection using sklearn neural network & OpenCV in Python and also Image Processing

IoT-based Banana Leaf disease detection using ML and Image Processing - Research Project

Plant Disease Detection and Fertilizer Suggestion is a smart agriculture project that uses image processing and machine learning to identify plant diseases from leaf images. Once a disease is detected, the system suggests the most suitable fertilizers to treat the issue and promote healthy crop growth.

The main objective of this project is to analyze and identify leaf diseases using image processing and machine learning. The project aims to help farmers and researchers by providing accurate disease detection, which can improve the quality and productivity of agricultural products.

Leaf Disease Detection and Grading using Image Processing

Plant Leaf Disease Detection model using Digital Image Processing using Python

Diseases detection in leaf using image processing techniques and machine learning algorithm

Detection and classification of coffee leaf disease such as Cercospora, Leaf Rust, and Phoma using YOLOv11 and image processing techniques

Leaf disease detection is a crucial aspect of modern agriculture and plant health management. It involves identifying and diagnosing various diseases affecting plant leaves using advanced technologies such as image processing, machine learning, and deep learning. Early detection of leaf diseases helps prevent crop loss, ensures optimal plant growth

The Plant Disease Detection project uses image processing and machine learning to identify plant diseases from leaf images. It helps farmers detect infections early, reducing crop damage and improving yield. The system analyzes visual symptoms, classifies diseases, and suggests remedies, ensuring better plant health.

AI-Driven Crop Disease Detection and Management uses machine learning and image processing techniques to identify crop diseases early from leaf images. The system analyzes symptoms, predicts disease type, and recommends treatments, helping farmers reduce crop loss, improve yield, and enable smarter, data-driven agricultural practices.

The Plant Disease Detection project uses image processing and machine learning to identify plant diseases from leaf images. It helps farmers detect infections early, reduce crop loss, and improve yield. The system can be developed as a mobile app or web tool for easy access.

This project classifies plant leaf diseases using the Vision Transformer (ViT) model. ViT processes images as sequences of patches, enabling high accuracy in image classification. The model aids early disease detection in crops, improving agricultural efficiency by reducing crop loss through precision farming.

The “Plant Leaf Disease Detection using Flask and Deep Learning” project is an intelligent web-based system developed to identify and classify diseases in plant leaves through advanced image processing and machine learning techniques. The primary aim of this project is to help farmers, researchers, and agricultural specialists detect plant disease.

Plant disease detection using AI and machine learning utilizes image processing and deep learning for early disease identification. The project includes a Django-based website (stored on GitHub) and an Android app for real-time leaf analysis, promoting sustainable agriculture by reducing pesticide use and improving crop yield

Built and fine-tuned a Convolutional Neural Network using PyTorch to classify rice diseases from leaf. Designed app functionality to capture images via a device camera, process them, and notify users of disease detection with recommended actions.eployed the model backend with Django to integrate with the mobile app for real-time predictions.

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A MATLAB GUI for detecting leaf diseases using image processing

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