A real time application of plants disease detection using deep learning

Project Title

A real time application of plants disease detection using deep learning

Project Area of Specialization Artificial IntelligenceProject Summary

Plants play an essential role in climate change, agriculture industry and a country’s economy. Thereby taking care of plants is very crucial. Just like humans, plants are effected by several disease caused by bacteria, fungi and virus. Identification of these disease timely and curing them is essential to prevent whole plant from destruction. Plant diseases is one kind of natural disasters that affect the growth of plants and even cause plant death during the whole growth process of plants from seed development to seeding and to seeding growth.

In this project, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies. Plant disease detection model is developed using neural network. First of all augmentation is applied on dataset to increase the sample size. Later Convolution Neural Network (CNN) is used with multiple convolution and pooling layers. Plant Disease dataset is used to train the model. After training the model, it is tested properly to validate the results.

In this project we use the Keras library.  Keras is a deep learning API written in Python, running on top of the machine learning platform Tensor Flow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research. Epoch 25 is use for measurement. The EPOCH 25 is a highly productive measurement solution, allowing the user to collect topographic data and stake layout detail.

We have performed different experiments using this model. 80% of training data and 20% of data from Plant Disease data is used for testing purpose that contains images of healthy as well as diseased plants.

Keywords: Plant Disease, Convolution Neural Network (CNN), Deep Learning, Keras library, Epoch 25, Agriculture, and Plant Disease.

Project Objectives

The objectives of the thesis are shown as following:

1. The model is able to detect several diseases from plants using pictures of their leaves.
2. Convolution Neural Network (CNN) is used with multiple convolution and pooling layers.
3. Use Keras is a deep learning API written in Python. Being able to go from idea to result as fast as possible is key to doing good research [3].
4. Using EPOCH 25 is a highly productive measurement solution, allowing the user to collect topographic data and stake layout detail.

Project Implementation Method

This part of the report illustrates the approach employed to classify the leaves into diseased or healthy and if the leaf is diseased, name of the disease is mentioned along with the remedies. Our system primarily revolves around the following five steps.

Benefits of the Project

Our solution of automated early disease detection is based on an artificial neural network, which is now the most robust technique for image classification. The main advantages of our solution include high processing speed and high classification accuracy. A plant disease recognition system can work as a universal detector, recognizing general abnormalities on the leaves, such as scorching or mold. However, our further research is related to precise recognition of particular diseases. After extensive training on diverse datasets our machine learning model will be capable of distinguishing a large number of different diseases.

Technical Details of Final Deliverable

The tools we used for deep learning are Tensor flow and Keras. Tensor Flow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of Tensor Flow. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it's built-in Python.

Tensor Flow have good scalability and support a large number of third-party libraries and deep network structures, and have the fastest training speed when training large CNN networks.

       Keras is a deep learning API written in Python, running on top of the machine learning platform Tensor Flow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research. Epoch 25 is use for measurement. The EPOCH 25 is a highly productive measurement solution, allowing the user to collect topographic data and stake layout detail.

Image processing and Machine learning techniques are used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image   pre-processing, image segmentation, feature extraction and classification.

Final Deliverable of the Project Software SystemCore Industry AgricultureOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Decent Work and Economic GrowthRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	0
Miscellaneous	Miscellaneous	0	20000	0