Utilizing Flutter Framework and Tensorflow Lite Convolutional Neural Networks-based Image Classification for Plant’s Leaf Disease Identification through Deep Learning - Mohammed Shoaib, Mohammed Faisal Uddin, Mohammed Azhar Uddin, Pathan Ahmed Khan (Mathematical Statistician and Engineering Applications, 2023)
Aim of this project is to develop an android application that detects plant diseases by using flutter,tensorflow lite and Convolutional Neural Networks (CNNs). The use of deep learning techniques, such as Convolutional Neural Networks (CNNs), for plant disease identification can help ensure healthy crop yields and prevent crop losses. In this study, researchers proposed a plant leaf disease identification system using CNNs and the Flutter framework for mobile app development. They trained a CNN model using transfer learning on a dataset of plant leaf images with various diseases, achieving high accuracy in identifying plant diseases. The Flutter app provided a user-friendly interface for accessing the model’s predictions, making it easy for farmers to use the system without any technical expertise. This cost-effective system can assist farmers in identifying plant disease at an early stage, leading to timely interventions and increased crop yields. Overall, the proposed system using CNNs and Flutter for mobile app development can play a crucial role in improving crop yields and reducing crop losses due to diseases, leading to increased profitability for farmers and a sustainable food supply for the growing population.