Automatic recognition of strawberry diseases and pests using convolutional neural network

• Published in: Smart agricultural technology Vol. 1; p. 100009
• Main Authors: Dong, Cheng, Zhang, Zhiwang, Yue, Jun, Zhou, Li
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021; Elsevier
• Subjects: Convolutional Neural Network; Deep Learning; Image Recognition; Strawberry Diseases and Pests; Transfer Learning; Image Recognition; Convolutional Neural Network; Strawberry Diseases and Pests; Transfer Learning; Deep Learning
• ISSN: 2772-3755; 2772-3755
• DOI: 10.1016/j.atech.2021.100009

•An improved AlexNet model which has good performance was designed for the recognition of the images of strawberry diseases and pests.•Based on inner product, an operator is proposed for improving the accuracy of image recognition.•After fine-tuned, the model has so few parameters that can be deployed to the mobile device. In this study, convolutional neural network (CNN) was used to classify and recognize the strawberry diseases and pests which cause tremendous economic losses for farmers. Based on AlexNet, the goal of this paper is to improve the recognition algorithm of strawberry diseases and pests by using the following methods: First, transfer learning is used to accelerate the time of model training and improve the recognition accuracy. Second, the model is fine-tuned to reduce the amount of training parameters and training time. The operator of combing inner product and ℓ∞ -norm was proposed and applied in the last fully connected layers to further improve the accuracy of recognition for strawberry diseases and pests. Compared with other start-of-the-art models, the experimental results show that our model achieves lower computational complexity and higher top-1 accuracy of the recognition for the strawberry diseases and pests on the test set.