AI BASED PLANT DISEASE DETECTION WITH ANDROID & IOS APP

Project Title

AI BASED PLANT DISEASE DETECTION WITH ANDROID & IOS APP

Project Area of Specialization Artificial IntelligenceProject Summary

Human society now has the ability to produce enough food to feed more than 7 billion people thanks to modern technologies. However, a number of issues, particularly climate change, continue to pose a danger to food security. Plant diseases are a global danger to food security, but they may also be devastating for smallholder farmers whose livelihoods are dependent on healthy crops. We report on the categorization of numerous illness photos using a convolutional neural network approach in order to construct accurate image classifiers for plant disease detection. The goal is to build a deep network in which the network's structure, as well as the functions (nodes) and edge weights, correctly map the input to the output. Our study is a first step toward smartphone-assisted plant disease detection systems with Android and iOS.

Project Objectives

Plant pests and diseases are very dangerous and harmful to food crops which could affect a large percentage of people in a particular region, and cause production and economic losses in the agricultural industry in different parts of the world. Besides, due to unsettled climatic and environmental conditions, the outbreaks of plants pests and diseases have become more frequent. Therefore, an early automated system which can detect, diagnose and aid in decreasing huge losses caused by plant diseases must be implemented. One of the aims of our system is to help in preventing the diseases from spreading speedily to other areas. Moreover, developing a plant disease identification and diagnosis system would be beneficial to users who have little knowledge about agricultural pests and diseases, and provides them with hints on how to diagnose and address the problems before they spread to other areas of the plant leaves, and it also benefits those who have limited access to agricultural experts.

With the evolution in computer technology, there has been a deep learning advancement in image plant-based disease and pattern recognition. Firstly, the aim of this project is to develop an effective model for image-based automatic detection, classification, and segmentation of plant diseases. Although training large neural networks could be time-consuming, the trained model will be able to detect and classify images within a fast pace of time, making it more efficient when deployed on mobile devices like smartphones. And secondly, the next aim of this project is to implement the developed framework on a mobile application which will serve as a tool for farmers, and a large percentage of the population, enabling a fast and effective plant disease identification and diagnosis, bringing about an easy decision-making process for controlling the harmful effects of diseases.

1. To design a plant disease detection system based on AI

2. To create intelligent Android and iOS applications

Project Implementation Method

The plant disease detector is built upon the Tensorflow framework and integrated into a mobile application. The SSD MobileNet model is trained and fine-tuned using the plant disease images from the dataset. The training is executed on Google Colab that comes with a free GPU. During the training, many of the training files happen to be large and are being saved automatically to google drive which occupied 22GB space in total, and after the training of the model, it is then converted into a TFLite which will be integrated in the mobile application that will be able to run on Android and iOS devices.
The detector model cannot be trained using just images, but we need to also provide annotation of the location and size of the diseased parts of a plant in each image. Although the Plant Village dataset does not provide the annotation files, we use LabelImg which is an image annotation software tool used to generate XML files of each corresponding image which will be fed into the model for training.
During preparation for training of the model, a total of 487 images are used with 390 of the images used for the training, and 97 of the images used for testing the plant disease detector. A plant leaf is considered to be infected when the detector model detects one or more spots in the image, with the detector indicating them with bounding boxes and name of disease with confidence value.

Benefits of the Project

Our goal in this project is to devise a method by develop a framework that is able to localized, and detect area of the plant leaf already affected by diseases. Our system makes use of plant diseases and pest images taken in an intended position and by this means, we avoid using other approaches in some related works like collecting samples and analyzing them in the laboratory. Deep learning has been a breakthrough in image processing, identification, and classification. It can efficiently deal with different light illumination conditions, objects size, background variations, and the surrounding area of the plant. Furthermore, our approach uses input images captured with different camera devices including digital cameras with various resolutions. In addition, after developing the framework, it will be made to be more effective by using smart-phone assisted disease diagnosis system which won?t only recognize and spot out the diseases on the infected areas quickly, but will also give hints on how the particular disease can be diagnosed in order to control it from spreading to other parts and areas. This will provide a practical real-time application that can be used in agricultural and several other fields without employing complex technology.

Technical Details of Final Deliverable

Mobile App:
The plant disease detector’s user interface is implemented as a self-contained mobile app developed using Flutter. Flutter is a mobile framework that allowed us to write a single codebase for the system’s business logic, and then deploy it as an iOS or Android app.

The mobile app allows farmers to capture a photo of the infected plants with proper alignment and orientation. The orientation handler, which runs as a background service thread in the mobile app, is responsible for correcting the tilt and camera angle of capturing the plant photo. Mobile app for detecting plant diseases. Which allows farmers to either capture a photo of the diseased plant or upload an existing image on the phone.

The software will identify the ailment and then display a description of the disease as well as the treatment method for that disease.

Final Deliverable of the Project Software SystemCore Industry AgricultureOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Zero Hunger, Good Health and Well-Being for People, Decent Work and Economic Growth, Responsible Consumption and Production, Life on LandRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	18863
Spent in Collecting Images for Dataset	Miscellaneous	1	8000	8000
Google Colab Pro	Equipment	1	1942	1942
Google Colab Pro+	Equipment	1	8921	8921