2,815 research outputs found
Deep interpretable architecture for plant diseases classification
Recently, many works have been inspired by the success of deep learning in
computer vision for plant diseases classification. Unfortunately, these
end-to-end deep classifiers lack transparency which can limit their adoption in
practice. In this paper, we propose a new trainable visualization method for
plant diseases classification based on a Convolutional Neural Network (CNN)
architecture composed of two deep classifiers. The first one is named Teacher
and the second one Student. This architecture leverages the multitask learning
to train the Teacher and the Student jointly. Then, the communicated
representation between the Teacher and the Student is used as a proxy to
visualize the most important image regions for classification. This new
architecture produces sharper visualization than the existing methods in plant
diseases context. All experiments are achieved on PlantVillage dataset that
contains 54306 plant images.Comment: 10 pages, 8 figures, Submitted to Signal Processing Algorithms,
Architectures, Arrangements and Applications (SPA2019),
https://github.com/Tahedi1/Teacher_Student_Architectur
Lemon Classification Using Deep Learning
Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of
many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing
demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate
modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon
classification approach is presented with a dataset that contains approximately 2,000 images belong to 3 species at a few
developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to
image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used
in farming automation have the latent to enhance crop harvest and improve output and productivity when designed
properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this
approach
Concept explainability for plant diseases classification
Plant diseases remain a considerable threat to food security and agricultural
sustainability. Rapid and early identification of these diseases has become a
significant concern motivating several studies to rely on the increasing global
digitalization and the recent advances in computer vision based on deep
learning. In fact, plant disease classification based on deep convolutional
neural networks has shown impressive performance. However, these methods have
yet to be adopted globally due to concerns regarding their robustness,
transparency, and the lack of explainability compared with their human experts
counterparts. Methods such as saliency-based approaches associating the network
output to perturbations of the input pixels have been proposed to give insights
into these algorithms. Still, they are not easily comprehensible and not
intuitive for human users and are threatened by bias. In this work, we deploy a
method called Testing with Concept Activation Vectors (TCAV) that shifts the
focus from pixels to user-defined concepts. To the best of our knowledge, our
paper is the first to employ this method in the field of plant disease
classification. Important concepts such as color, texture and disease related
concepts were analyzed. The results suggest that concept-based explanation
methods can significantly benefit automated plant disease identification.Comment: Accepted at VISAPP 202
DC-SAM: DILATED CONVOLUTION AND SPECTRAL ATTENTION MODULE FOR WHEAT SALT STRESS CLASSIFICATION AND INTERPRETATION
Salt stress can impact wheat production significantly and is difficult to be managed when the condition is critical. Hence, detecting such stress whet it is at an early stage is important. This paper proposed a deep learning method called Dilated Convolution and Spectral Attention Module (DC-SAM), which exploits the difference in spectral responses of healthy and stressed wheat. The proposed DC-SAM method consists of two key modules:Â (i)Â a dilated convolution module to capture spectral features with large receptive field;Â (ii)Â a spectral attention module to adaptively fuse the spectral features based on their interrelationship. As the dilated convolution module has long receptive fields, it can capture short- and long dependency patterns that exist in hyperspectral data. Our experimental results with four datasets show that DC-SAM outperforms existing state-of-the-art methods. Also, the output of the proposed attention module reveals the most discriminative spectral bands for a given wheat stress classification task
Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants
In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets
Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco
The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses
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