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Deep learning model to aid in the identification of diseased plants. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Nov 3, 2022 Item updated: Dec 30, 2024 Number of downloads: 2,708

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Description

Fruit and vegetable plants are vulnerable to diseases that can negatively affect crop yield, causing planters to incur significant losses. These diseases can affect the plants at various stages of growth. Planters must be on constant watch to prevent them early, or infestation can spread and become severe and irrecoverable. There are many types of pest infestations of fruits and vegetables, and identifying them manually for appropriate preventive measures is difficult and time-consuming.

This pretrained model can be deployed to identify plant diseases efficiently for carrying out suitable pest control. The training data for the model primarily includes images of leaves of diseased and healthy fruit and vegetable plants. It can classify the multiple categories of plant infestation or healthy plants from the images of the leaves.

Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. 

Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.

Input
8 bit, 3-band (RGB) image. Recommended image size is 224 x 224 pixels.

Note: Input images should have grey or solid color background with one full leaf per image.  

Output
Classified image of the leaf with any of the plant disease, healthy leaf, or background classes as in the Plant Leaf Diseases dataset.

Applicable geographies
This model is expected to work well in all regions globally. However, results can vary for images that are statistically dissimilar to training data.

Model architecture
This model uses the ResNet50 model architecture implemented in ArcGIS API for Python.

Accuracy metrics
This model has an overall accuracy of 97.88 percent. The confusion matrix below summarizes the performance of the model on the validation dataset. Confusion matrix of Plant leaf disease classsification


Sample results
Here are a few results from the model:

Ground truth: Apple_black_rot / Prediction: Apple_black_rot

apple_black_rot

Ground truth: Potato_early_blight / Prediction: Potato_early_bight

potato_early_blight

Ground truth: Raspberry_healthy / Prediction: Raspberry_healthy

raspberyy_healthy

Ground truth: Strawberry_leaf_scorch / Prediction: Strawberry_leaf_scorch

strawberry_leaf_scorch

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Terms of Use

https://downloads.esri.com/blogs/arcgisonline/esrilogo_new.png This work is licensed under the Esri Master License Agreement.

No special restrictions or limitations on using the item's content have been provided.

Comments (2)

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arincon_esrichiletesting Item Owner commented a year ago Delete Reply

He probado el modelo, aclaro solo con las imagenes descargadas en la guia, pero: 1. El modelo solo permite analizar una imagen a la vez y con el tiempo que tarda en correr sería demasiado tardio extraer toda la informacion de un conjunto de datos... se puede mejorar para hacerlo de forma masiva. 2. Si se logra hacer de forma masiva deberia quedar toda la información en una tabla que asocie el nombre de la imagen con el resultado. Muchas gracias

spathak_deldev Item Owner commented a year ago Delete

Hi, we have a blog which shows how the model can be run on a large dataset and the results can be generated as a feature layer. Link for the blog: https://www.esri.com/arcgis-blog/products/arcgis-pro/imagery/identify-plant-species-using-deep-learning-tools-in-arcgis-pro/

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          Deep Learning Package, LivingAtlasDLPK, dlpk, Plant Leaf Disease Classification

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