Search results
A project to train and evaluate different dnn models for plant disease detection problem, tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data and implement segmentation pipeline to avoid miss-classification due to unwanted input
A project to detect plant disease using leaf images and to calculate the percentage of leaf affected by the disease using OpenCV.
17 gru 2021 · This study aims to develop an android application to detect and identify plant diseases through deep convolutional neural network.
12 gru 2023 · In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization...
Our project will develop an open-source web application that leverages advanced Convolutional Neural Networks to provide accurate and efficient plant disease detection and diagnosis, facilitating agricultural productivity, and serving as an educational tool for technology and agriculture enthusiasts. Our GitHub. You may access our GitHub repo here.
Here, we demonstrate the technical feasibility using a deep learning approach utilizing 54,306 images of 14 crop species with 26 diseases (or healthy) made openly available through the project PlantVillage (Hughes and Salathé, 2015). An example of each crop—disease pair can be seen in Figure 1.
In this project we have analyzed different image parameters or features to identifying different plant leaves diseases to achieve the best accuracy. Previously plant disease detection is done by visual inspection of the leaves or some chemical processes by experts.
