class="nav-up">

Using Deep Learning for Image-Based Plant Disease Detection

25

Feb. 21

2.86 K

VIEWS

Introduction

Disease detection in plants plays a very important role in agriculture. Crop diseases serve as a major threat to the food supply. Identifying disease by just looking at images of plants can lead to quicker interventions that can help farmers a lot. We will use neural networks for plant disease recognition in the context of image classification.

Dataset

We can use any public dataset available online for this project like https://www.kaggle.com/emmarex/plantdisease
https://www.kaggle.com/vipoooool/new-plant-diseases-dataset

Next thing is to import the necessary packages

  1. Numpy: A library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. (Source: Wikipedia )
  2. Sklearn: A free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. (Source: Wikipedia )
  3. Keras: Keras is an open-source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. (Source: Wikipedia
  4. Matplotlib: A plotting library for the Python programming language and its numerical mathematics extension.

Network Architecture

  1. We split the data-set into three sets — train, validation and test sets.
  2. We tried with pre-trained models like Inception v3. The last layer is used for the classification with softmax as the activation function.
  3. The loss function used is binary cross-entropy and trained the model for 50 epochs.
  4. For this architecture, we’ve used 30 per cent dropouts to reduce overfitting in between the layers and batch normalization to reduce internal covariate shift.

(source – https://towardsdatascience.com/plant-disease-detection-using-transfer-learning-e6995642a71e )

Model From scratch

(Plant-ai)

Wrapping Up

If you want to know more about our skills and our ideas in Deep/Machine Learning, then why wait? Get in touch with us. Reach us at info@letsnurture.com. We would be glad to stroll with you.

Author

Lets Nurture
Posted by Lets Nurture

Blog A directory of wonderful things

AI for Pneumonia Detection

The risk of pneumonia is enormous for many, especially in the nations where billions face energy poverty and rely on polluting forms of energy. “The WHO estimates that over 4 …

10 most extensively used Python libraries

Python, being one of the most sought after programming languages, has a huge collection of libraries. In fact, this expansive set of libraries can be considered as one of the …

AI for Food Detection

“Four simple ways to fight diabetes/Obesity: Go for regular medical check-ups; Exercise more; Watch your diet, and Cut down on soft drinks.” – Singapore PM Lee Hsien Loong (Nvidia Research …

We use cookies to give you tailored experiences on our website.
Okay