Attention-based recurrent neural network for plant disease classification

Lee Sue Han, Goeau Hervé, Bonnet Pierre, Joly Alexis. 2020. Attention-based recurrent neural network for plant disease classification. Frontiers in Plant Science, 11:601250, 8 p.

Url - jeu de données - Entrepôt autre : https://github.com/AdelineMomo/CNN-plant-disease

Quartile : Q1, Sujet : PLANT SCIENCES

Résumé : Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

Mots-clés Agrovoc : maladie des plantes, pathologie végétale, identification, réseau de neurones, classification, surveillance des cultures, mesure phytosanitaire, agent pathogène

Mots-clés complémentaires : deep learning

Mots-clés libres : Plant disease classification, Deep Learning, Recurrent neural network, Precision agriculture technologies, Crops monitoring

Classification Agris : H20 - Maladies des plantes
H50 - Troubles divers des plantes
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Auteurs et affiliations

• Lee Sue Han, Swinburne University of Technology Sarawak Campus (MYS)
• Goeau Hervé, CIRAD-BIOS-UMR AMAP (FRA)
• Bonnet Pierre, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-2828-4389 - auteur correspondant
• Joly Alexis, INRIA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/598375/)

Métriques

[ Page générée et mise en cache le 2024-04-17 ]