Convolution neural network deployment for plant leaf diseases detection

Abstract

The automated identification of plant diseases based on plant leaves is a huge breakthrough. Furthermore, early and accurate detection of plant diseases positively impacts crop productivity and quality. However, managing the accessibility of early plant disease detection is crucial. This work has environmental goals aiming to save plants from different threatening diseases by providing early detection of the affected leaves. We studied the performance of different Convolutional Neural Network (CNN) architectures in predicting 26 diseases for 14 plant species. The work studied the complexity of the system and compared the two main deep learning frameworks, TensorFlow and PyTorch, to get the most accurate results with higher accuracy. Using the “New PlantVillage Dataset” from Kaggle [1], the TensorFlow models achieved an accuracy of 90,94% for the basic CCN architecture, and 95,59% for the Transfer Learning architecture with VGG19. Whereas the PyTorch models achieved an accuracy of 93,47% for the basic CCN architecture, and 98,53% for the Transfer Learning architecture with ResNet34. Finally, after examining the feasibility of the model’s implementation and discussing the main problems that may be encountered, the models were deployed in a mobile application using the Tflite and torch mobile flutter SDK to let them as an internal feature in the mobile without the need for any access to the cloud, which is known as edge AI