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An application that for farmers to detect the type of plant or crops, detect any kind of diseases in them. The app sends the image of the plant to the server where it is analysed using CNN classifier model. Once detected, the disease and its solutions are displayed to the user
CropCareAI is an AI-powered web application built using Flask to assist plant enthusiasts, farmers, and researchers in identifying and diagnosing plant diseases using pretrained Machine Learning models.
🌿 Plant Disease Detection from Images 🌿. 📋 Project Overview. Our project focuses on creating an intuitive Streamlit application that empowers users to upload images of plant leaves for accurate identification of plant diseases.
Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof).
6 wrz 2021 · We introduce a new dataset in the literature for the diagnosis and monitoring of plant symptoms, called DiaMOS Plant. It is a dataset collected under realistic field conditions, composed of 3505 images depicting 4 leaf stresses and 3 stages of fruit development: fruit set, growth and ripening.
21 sie 2019 · The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data.
Plant_Disease_Detection.ipynb - Colab. Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True). #Locate the Dataset...
