Deep learning models for plant disease detection and diagnosis

By K. P. Ferentinos. Computers and Electronics in Agriculture, 2018

2,850 citations recorded by the source corpus.

Subfields: Cell Biology, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/3f611458b84ca8756c863916b33d12c704687127

How researchers see this paper

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

In Cell Biology

1. J. G. Arnal Barbedo
2. Jinzhu Lu
3. Johan Potgieter
4. Sajid Hasan Apon
5. Yuehan Chen
6. Kemal Akyol
7. Emine Uçar
8. Pierre-Luc St-Charles

In Ecology

1. Dionysis D. Bochtis
2. Jinzhu Lu
3. Johan Potgieter
4. Sajid Hasan Apon
5. Yuehan Chen
6. Kemal Akyol
7. Emine Uçar
8. Pierre-Luc St-Charles

In Plant Science

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Khalid Mahmood Arif
4. Paulo Ferreira
5. Flavio de Barros Vidal
6. Andre da Silva Abade
7. Jun Liu
8. Xuewei Wang

Citations in the corpus

• Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “mages Linguagem & Framework Predominant Approach S61 [102] 2018 4 Based LeNet +LVQ 86,0% PlantVillage Subset 500 Images / 5 Class Controlled Environment Tomato RGB TensorFlow New Architecture S62 [103] 2018 4 AlexNet 99,44% PlantVillage 87.848 Images / 58 Class Controlled Environment GoogLeNet 9”
• Machine Learning in Agriculture: A Review (2018, methodology): “Finally, the author of [90] presented a CNN-based method for the disease detection diagnosis based on simple leaves images with sufficient accuracy to classify between healthy and diseased leaves in various plants.”
• Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network (2020, background): “…and pests, and have made a lot of achievements in recent years (Mohanty et al., 2016; Sladojevic et al., 2016; Wang et al., 2017; Liu et al., 2018; Ferentinos, 2018; Brahimi et al., 2018; Kaur et al., 2019), especially in the disease and insect image recognition of apple, tomato, cucumber, and…”
• Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review (2018, neutral): “Recent works like automatic diseases detection and identi fi - cation (Lu et al., 2017; Ferentinos, 2018), microorganisms classi fi cation (Kosov et al., 2018), detection of stored-grain insects using deep learning (Shen et al., 2018) are some of the examples.”
• A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “A comparison of VGGNet with GoogLeNet shows that GoogLeNet trains faster than VGGNet.”
• Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network (2021, background): “The authors suggest constructing a real-time plant disease diagnosis system based on the proposed model [43].”