Maize leaf disease classification using convolutional neural networks and hyperparameter optimization

Maize is an important food crop in the world, but several diseases affect the quality and quantity of agricultural production. Identifying these diseases is a very subjective and time-consuming task. The use of computer vision techniques allows automatizing this task and is essential in agricultural applications. In this study, we assess the performance of three state-of-the-art convolutional neural network architectures to classify maize leaf diseases. We apply enhancement methods such as Bayesian hyperparameter optimization, data augmentation, and fine-tuning strategies. We evaluate these CNNs on the maize leaf images from PlantVillage dataset, and all experiments were validated using a five-fold cross-validation procedure over the training and test sets. Our findings include the correlation between the maize leaf classes and the impact of data augmentation in pre-trained models. The results show that maize leaf disease classification reached 97% of accuracy for all CNNs models evaluated. Also, our approach provides new perspectives for the identification of leaf diseases based on computer vision strategies.

Download Full-text

Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet

PeerJ Computer Science ◽

10.7717/peerj-cs.371 ◽

2021 ◽

Vol 7 ◽

pp. e371

Author(s):

Elia Cano ◽

José Mendoza-Avilés ◽

Mariana Areiza ◽

Noemi Guerra ◽

José Longino Mendoza-Valdés ◽

...

Keyword(s):

Cross Validation ◽

Data Augmentation ◽

Skin Diseases ◽

Medical Image Analysis ◽

Skin Lesions ◽

Disease Classification ◽

Fine Tuning ◽

Skin Cancer Prevention ◽

Skin Imaging ◽

Multi Class Classification

Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. This article presents an approach that uses CNNs with a NASNet architecture to recognize in a more accurate way, without segmentation, eight skin diseases. The model was trained end-to-end on Keras with augmented skin diseases images from the International Skin Imaging Collaboration (ISIC). The CNN architectures were initialized with weight from ImageNet, fine-tuned in order to discriminate well among the different types of skin lesions, and then 10-fold cross-validation was applied. Finally, some evaluation metrics are calculated as accuracy, sensitivity, and specificity and compare with other CNN trained architectures. This comparison shows that the proposed system offers higher accuracy results, with a significant reduction on the training paraments. To the best of our knowledge and based in the state-of-art recompiling in this work, the application of the NASNet architecture training with skin image lesion from ISIC archive for multi-class classification and evaluated by cross-validation, represents a novel skin disease classification system.

Download Full-text

Assessing the Impact of Segmentation on Wheat Stripe Rust Disease Classification using Computer Vision and Deep Learning

IEEE Access ◽

10.1109/access.2021.3134196 ◽

2021 ◽

pp. 1-1

Author(s):

Hassan Raza Bukhari ◽

Rafia Mumtaz ◽

Salman Inayat ◽

Uferah Shafi ◽

Ihsan Ul Haq ◽

...

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Stripe Rust ◽

Disease Classification ◽

Rust Disease ◽

Wheat Stripe Rust ◽

The Impact

Download Full-text

Maize leaf disease classification using deep convolutional neural networks

Neural Computing and Applications ◽

10.1007/s00521-019-04228-3 ◽

2019 ◽

Vol 31 (12) ◽

pp. 8887-8895 ◽

Cited By ~ 11

Author(s):

Ramar Ahila Priyadharshini ◽

Selvaraj Arivazhagan ◽

Madakannu Arun ◽

Annamalai Mirnalini

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Disease Classification ◽

Deep Convolutional Neural Networks ◽

Maize Leaf ◽

Leaf Disease

Download Full-text

Optimizing data augmentation policies for convolutional neural networks based on classification of sickle cells

10.5753/wvc.2020.13479 ◽

2020 ◽

Author(s):

Matheus Da Silva ◽

Larissa Rodrigues ◽

João Fernando Mari

Keyword(s):

Sickle Cell ◽

Network Architecture ◽

Data Augmentation ◽

Bayesian Optimization ◽

Optimization Approach ◽

Hyperparameter Optimization ◽

Bayesian Optimization Algorithm ◽

Sickle Cells ◽

Validation Procedure ◽

Image Transformations

Data augmentation is a key procedure in many image classification tasks, mainly to overcome the problem of small datasets. In this work, we exploit the data augmentation as a hyperparameter optimization approach. We tested our methods to classify erythrocytes to assist the diagnosis of sickle cell anemia. In this study, we proposed a data augmentation approach based on hyperparameter optimization to find the best augmentation policies through the Bayesian optimization algorithm. We also developed a convolutional neural network architecture from scratch and compared it with two classic architectures to classify sickle cell images. Our approach defines the best data augmentation solutions and sends those solutions to be carried out by CNN in the final training. All experiments were validated using a stratified five-fold cross-validation procedure, and our best result achieves 92.54% of accuracy. The results suggest the best augmentation policies defined with optimization allow us to obtain superior results than other strategies such as without data augmentation, several randomly defined image transformations, and only random rotations. As far as we know, our paper is the first to propose optimizing data augmentation policies in biomedical images leading to a better exploration of these strategies in several fields.

Download Full-text

Maize leaf disease classification using convolutional neural network

10.1063/5.0068599 ◽

2021 ◽

Author(s):

S. Malliga ◽

P. S. Nandhini ◽

S. V. Kogilavani ◽

R. Jaya Harini ◽

S. Jaya Shree ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Disease Classification ◽

Maize Leaf ◽

Leaf Disease

Download Full-text

Texture based Leaf Disease Classification using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9446.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 956-961

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Disease Classification ◽

Machine Learning Techniques ◽

Textural Features ◽

Leaf Disease ◽

Learning Techniques ◽

Segmented Images ◽

Agricultural Applications

Machine learning techniques has emerged as a potential field in many of present day agricultural applications. One of these applications is the identification and classification of leaf diseases. In this paper, a triangular based and OTSU based methods are applied for segmentation, Textural features primarily based on GLCM are obtained for these segmented images using kmeans clustering technique, further classification of different leaf disease is performed using an SVM based classification. The proposed method resulted in an overall classification accuracy of 70% using the triangular based segmentation with an AUC of 0.63.

Download Full-text

Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

Remote Sensing ◽

10.3390/rs13020260 ◽

2021 ◽

Vol 13 (2) ◽

pp. 260

Author(s):

Ha Trang Nguyen ◽

Maximo Larry Lopez Caceres ◽

Koma Moritake ◽

Sarah Kentsch ◽

Hase Shu ◽

...

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Data Augmentation ◽

Detection Algorithm ◽

Natural Phenomenon ◽

Insect Outbreaks ◽

Surface Models ◽

Uav Images ◽

The Cost ◽

The Impact

Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest.

Download Full-text

Effect of data-augmentation on fine-tuned CNN model performance

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i1.pp84-92 ◽

2021 ◽

Vol 10 (1) ◽

pp. 84

Author(s):

Ramaprasad Poojary ◽

Roma Raina ◽

Amit Kumar Mondal

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

High Performance ◽

Data Augmentation ◽

Model Performance ◽

Training Data ◽

Fine Tuning ◽

Test Accuracy ◽

Training Time

During the last few years, deep learning achieved remarkable results in the field of machine learning when used for computer vision tasks. Among many of its architectures, deep neural network-based architecture known as convolutional neural networks are recently used widely for image detection and classification. Although it is a great tool for computer vision tasks, it demands a large amount of training data to yield high performance. In this paper, the data augmentation method is proposed to overcome the challenges faced due to a lack of insufficient training data. To analyze the effect of data augmentation, the proposed method uses two convolutional neural network architectures. To minimize the training time without compromising accuracy, models are built by fine-tuning pre-trained networks VGG16 and ResNet50. To evaluate the performance of the models, loss functions and accuracies are used. Proposed models are constructed using Keras deep learning framework and models are trained on a custom dataset created from Kaggle CAT vs DOG database. Experimental results showed that both the models achieved better test accuracy when data augmentation is employed, and model constructed using ResNet50 outperformed VGG16 based model with a test accuracy of 90% with data augmentation & 82% without data augmentation.

Download Full-text

Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

10.5194/egusphere-egu21-14091 ◽

2021 ◽

Author(s):

Ha Trang Nguyen ◽

Maximo Larry Lopez Caceres ◽

Koma Moritake ◽

Sarah Kentsch ◽

Hase Shu ◽

...

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Data Augmentation ◽

Detection Algorithm ◽

Natural Phenomenon ◽

Insect Outbreaks ◽

Surface Models ◽

Uav Images ◽

The Cost ◽

The Impact

Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest.

Download Full-text

Research on Self-Supervised Comparative Learning for Computer Vision

Journal of Electronic Research and Application ◽

10.26689/jera.v5i3.2320 ◽

2021 ◽

Vol 5 (3) ◽

pp. 5-17

Author(s):

Yuanyuan Liu ◽

Qianqian Liu

Keyword(s):

Computer Vision ◽

Supervised Learning ◽

Data Augmentation ◽

Large Data ◽

Large Data Sets ◽

Fine Tuning ◽

Data Sets ◽

Semantic Features ◽

Learning Method ◽

Learning Methods

In recent years, self-supervised learning which does not require a large number of manual labels generate supervised signals through the data itself to attain the characterization learning of samples. Self-supervised learning solves the problem of learning semantic features from unlabeled data, and realizes pre-training of models in large data sets. Its significant advantages have been extensively studied by scholars in recent years. There are usually three types of self-supervised learning: “Generative, Contrastive, and Generative-Contrastive.” The model of the comparative learning method is relatively simple, and the performance of the current downstream task is comparable to that of the supervised learning method. Therefore, we propose a conceptual analysis framework: data augmentation pipeline, architectures, pretext tasks, comparison methods, semi-supervised fine-tuning. Based on this conceptual framework, we qualitatively analyze the existing comparative self-supervised learning methods for computer vision, and then further analyze its performance at different stages, and finally summarize the research status of self-supervised comparative learning methods in other fields.

Download Full-text