Deep learning-based mobile application for plant disease diagnosis.

Abstract

Plants are susceptible to a variety of diseases in farming. The impact of sudden climatic change harms their growth causing dangerous viruses and pests. Plant diseases are one of the most serious problems confronting agriculture across the world and harming the health, economy, and livelihood of the human population. The majority of traditional plant pests' diagnosis methods rely on human visual observation and inspection. However, this approach is time-consuming and requires strong agricultural skills and significant human efforts. Recent breakthroughs in computer vision and Deep Learning provide a potential pathway for developing a plant disease diagnosis system that will be able to detect plant diseases in different geographical regions with fewer human interventions. This dissertation presents a Deep Learning powered mobile-based system to automate the early identification of plant diseases. The developed system uses Convolutional Neural Networks to classify plant leaves into 38 classes with 14 types of plants. To train and test our models, we used two datasets from Kaggle containing about 87000 and 20803 images of healthy and diseased plant leaves respectively. After the training, we assessed our models using some classification evaluation metrics such as the accuracy and f1-score and found that they were able to correctly classify most of the images in the test set. Finally, to increase the usability of our algorithm, we developed a smartphone application that runs on both Android and iOS operating systems with a simple user interface using the Flutter framework. This application allows farmers to capture pictures of their infected plant leaves or import them from their phone library and then it displays the disease category with the plant name and the accuracy of the prediction. This approach is supposed to provide farmers with a better opportunity to maintain their crops' health and minimize the use the incorrect fertilizers that might damage the plants and the environment.