Plant Leaf Disease Detection App using Deep Learning

Our project is based on finding the different diseases of plants like Apple, Corn, Grapes, Orange, Potato, Tomato, Cherry, etc. using Deep Learning (CNN) and implementation in Android.

The android application is developed to provide users with an easy way to interact with the app to detect what is going on in their plant leaf.

This app helps farmers to protect their crops. This app comes with a handy camera integrated to allow the farmer to click an image of the affected crop that he wishes to diagnose.

This image is processed in the backend using a machine learning model (tflite model) to classify the leaf disease (make sure we take the picture of the affected leaf, otherwise it will give a hazardous result). Further, it gives information about the steps needed to follow to improve plant health.

Features

1. Admin: Admin is the one who administers the system and inputs updates.
2. User login: Users have to create an account in the system by registering themselves. Then they may log in to the system and can utilize services. The user’s data is stored in the Firebase database.
3. Detection: The system contains a TensorFlow lite model that can detect the disease from the leaf image that is taken by the camera using the app. After taking the image from the camera it processes the image using a deep learning algorithm then from the model we get the three best results. Using the Priority queue method from the three best results we picked the highest percentage and show it to the user.
4. Precaution: After getting the result of the image, we give a precaution option to the user to get the cure for the disease.
5. Crop Information: there is a page where we give information on the different crops related to our project. Like plant normal temperature to grow, scientific name, their planning nature, culture, etc.
6. Community: In the application, we also added a feature to communicate with other people if someone has any doubts about anything. The user just to rise their question with a proper image, heading, and description.
7. Pest information: We also try to add pests information related to the diseases that are seen in the plants.

Software Requirements

1. Windows 9/10/11
2. PyCharm/Jupyter
3. Android Studio (4.2.1 or above)
4. Firebase

Hardware Components

1. Processor — i5
2. Memory — 8GB RAM

Advantages

1. Provides instant results to the affected leaf.
2. It can detect more than 10 diseases in a single app.
3. Reduces tedious work for farmers as well as others to detect the disease of their plant.
4. Provides quick access and is affordable.
5. The system saves time and reduces human efforts.

Disadvantages

1. It requires an internet connection.
2. Sometimes false image gives false results.

Applications

· It can be used by Farmers, agriculture workers, researchers, etc.

Watch the demo video to know more

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Reference

• J. Amara, B. Bouaziz, and A. Algergawy, “A deep learning-based approach for banana leaf diseases classification,” Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband, 2017.
• B. A. Ashqar and S. S. Abu-Naser, “Image-Based Tomato Leaves Diseases Detection Using Deep Learning,” 2019.
• Y. Kawasaki, H. Uga, S. Kagiwada, and H. Iyatomi, “Basic study of automated diagnosis of viral plant diseases using convolutional neural networks,” in International Symposium on Visual Computing, 2015, pp. 638–645.
• V. Singh and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Information processing in Agriculture, vol. 4, no. 1, pp. 41–49, 2017.
• P. B. Padol and A. A. Yadav, “SVM classifier based grape leaf disease detection,” in 2016 Conference on Advances in Signal Processing (CASP), Jun. 2016, pp. 175–179, doi: 10.1109/CASP.2016.7746160.
• S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in plant science, vol. 7, p. 1419, 2016.
• G. Wang, Y. Sun, and J. Wang, “Automatic image-based plant disease severity estimation using deep learning,” Computational intelligence and neuroscience, vol. 2017, 2017.