Identification of rice diseases using deep convolutional neural networks

By Yang Lu, Shujuan Yi, Nianyin Zeng, Yurong Liu, Yong Zhang. Neurocomputing, 2017

921 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/d78ef3d65031f9e0fac52a60d9eebe9dc6527f9a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Shujuan Zhang
4. Paulo Ferreira
5. Flavio de Barros Vidal
6. Andre da Silva Abade
7. Lili Li
8. Mingming Zhang

In Plant Science

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Khalid Mahmood Arif
4. Sajid Hasan Apon
5. Shujuan Zhang
6. Marcin Wozniak
7. F. Nowrin
8. Geeta Rani

Citations in the corpus

• Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks (2018, methodology): “[25] used different pooling operations, filter sizes, and algorithms to identify 10 common rice diseases.”
• Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[44] used CNN to identify rice diseases, early disease detection, and the characteristic maps of disease spots were also obtained.”
• Identification and recognition of rice diseases and pests using convolutional neural networks (2018, methodology): “[16] conducted a study on detecting 10 different rice plant diseases using a small handmade CNN architecture, inspired by older deep learning frameworks such as LeNet-5 and AlexNet, using 500 images.”
• Plant Disease Detection and Classification by Deep Learning (2019, methodology): “Feature map for the diseased rice plant [78]”
• Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks (2017, background): “Chosen from experience, the size of the images was compressed from 2456 × 2058 to 256 × 256, which is able to be divided by 2 and reduces the training time [16].”
• A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “It is a lie t i li t t e fe t res str ct res f r i g t e lear ing rocess easier [17].”
• Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “Among the 121 studies evaluated, it is possible to notice that only 25 studies (S1, S2, S6, S7, S11, S12, S13, S15, S22, S35, S43, S54, S61, S65, S68, S70, S75, S84, S86, S91, S100, S113, S114, S116, S120) propose a new architecture.”