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
- plant-disease-identification · GitHub Topics · GitHub
PlanteD is an innovative plant leaf disease detection app...
- plant-disease-identification · GitHub Topics · GitHub
This project aims to develop a robust plant disease detection system using advanced machine learning techniques, primarily leveraging YOLO for object detection. The workflow includes data preprocessing, feature extraction, non-negative matrix factorization, fuzzy clustering, and model training.
PlanteD is an innovative plant leaf disease detection app developed using the powerful Flutter framework, combining the prowess of Artificial Intelligence (AI) and Machine Learning (ML). Designed for plant enthusiasts, gardeners, and farmers, PlanteD revolutionizes the way we identify and combat leaf diseases, ensuring healthier plants.
This app allows farmers and gardens to remotely monitor, identify and treat plant diseases in the field. The app offers a personalized experience by tailoring plant disease diagnostics and treatment plans to specific to an individual's needs.
We opte to develop an Android application that detects plant diseases. The project is broken down into multiple steps: Building and creating a machine learning model using TensorFlow with...
8 lis 2019 · We can identify plant diseases. 🎉. It’s a new feature of Plant.id API and we call it plant health assessment. The current beta version is capable of identifying exactly 100 plant diseases with ~ 57.1% accuracy (considering top3 values).
7 lis 2023 · This notebook shows you how to fine-tune CropNet models from TensorFlow Hub on a dataset from TFDS or your own crop disease detection dataset. You will: Load the TFDS cassava dataset or your own data. Enrich the data with unknown (negative) examples to get a more robust model. Apply image augmentations to the data.
