Cucumber leaf disease identification with global pooling dilated convolutional neural network
By Shanwen Zhang, Subing Zhang, Chuanlei Zhang, Xianfeng Wang, Yun Shi. Computers and Electronics in Agriculture, 2019
309 citations recorded by the source corpus.
Abstract It is a challenging research topic to identify plant disease based on diseased leaf image processing techniques due to the complexity of the diseased leaf images. Deep learning models are promising for identifying plant disease based on leaf images and AlexNet is one of these models. Aiming at the problems of too many parameters of the AlexNet model and single feature scale, a global pooling dilated convolutional neural network (GPDCNN) is proposed in this paper for plant disease identification by combining dilated convolution with global pooling. Compared with the classical convolutional neural network (CNN) and AlexNet models, GPDCNN has three improvements: (1) the convolution receptive field are increased without increasing the computational complexity and without losing the discriminant formation by replacing fully connected layers with a global pooling layer; (2) dilated convolutional layer is employed to recover the spatial resolution without increasing the number of training parameters; (3) GPDCNN also integrates the merits of dilated convolution and global pooling. Experimental results on the datasets of six common cucumber leaf diseases demonstrate that the proposed model can effectively recognize cucumber diseases.
Subfields: Plant Science
View Paper: https://www.semanticscholar.org/paper/e8405e2ff387132fdfe15ded09dc1284fb78f2dc
How researchers see this paper
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In Plant Science
- Johan Potgieter
- Muhammad Hammad Saleem
- Shujuan Zhang
- Paulo Ferreira
- Flavio de Barros Vidal
- Andre da Silva Abade
- Lili Li
- Bin Wang
Citations in the corpus
- Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “S1 [48] 2019 5 GPDCNN 94,65% Customized 700 Images / 7 Class Real Environment Cucumbers RGB TensorFlow New Architecture”
- Plant Disease Detection and Classification by Deep Learning (2019, methodology): “The cucumber plant diseases were identified in [81] by using the dilation convolutional neural network.”
- Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “[71] combined the expansion convolution and global pooling for the problem of the AlexNet model with too many parameters and a single feature scale and proposed a global pooling extended convolutional neural net-”