Apple leaf diseases detection using convolutional neural networks

Abstract

Effective and prompt detection of apple leaf diseases is key to their prevention and treatment, as well as to determine the extent of the damage caused. Today, this task is difficult due to the limited number of available methods and tools. Automated disease detection methods can improve fruit quality and reduce human error. This paper proposes the use of an improved convolutionl neural network consisting of 15 layers. To achive maximum accuracy in identifying and classifying diseases of apple leaves, it’s proposed to use the CNN as a basic for the Single Shot Detector (SSD) algorithm. Benchmarking with models such as AlexNet and ResNet-50 showed that the proposed model achieves an accuracy of 96.62%, which outperforms other similar models.