scholarly journals Identification of Plant disease in leaves, using Deep Neural Networks

2020 ◽  
Vol 8 (5S) ◽  
pp. 18-21
Keyword(s):  
Neural Networks ◽  
Efficient Algorithm ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Research Topic ◽  
Plant Diseases ◽  
Plant Leaves ◽  
Crop Fields ◽  
Large Crop

Plant diseases have become a concern as they can lead to a significant reduction in both the quality and quantity of agricultural products.Immediate identification of plant diseases is a key research topic as it can prove useful in the monitoring of large crop fields and thus automatically identify the signs of pathogens as soon as they appear on plant leaves. The proposed efficient algorithm could successfully identify and recognize the diseases under investigation and model could achieve an accuracy of 95.18.

Porting deep neural networks on the edge via dynamic K-means compression: A case study of plant disease detection

2021 ◽  
pp. 101437
Author(s):  
Fabrizio De Vita ◽  
Giorgio Nocera ◽  
Dario Bruneo ◽  
Valeria Tomaselli ◽  
Davide Giacalone ◽  
...  
Keyword(s):  
Neural Networks ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Disease Detection

scholarly journals An overview of activation functions used in Neuronal Networks: Application for plant disease diagnosis

2021 ◽  
Vol 10 (1) ◽  
pp. 139-145
Keyword(s):  
Plant Disease ◽  
Transfer Functions ◽  
Activation Function ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Automatic Identification ◽  
Relevant Parameter ◽  
Activation Functions ◽  
Crop Fields ◽  
Learning Techniques

Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Many applications in automatic identification of plant diseases have been developed. This work adopts a new approach that focuses on studying a relevant parameter that make a significant impact on the performance of CNNs, namely, the variants of activation function, particularly the most famous used functions and their influence on the model’s performance and accuracy. We will also present the different types of activation functions, which are also called transfer functions. Then, and through a case study application to the plant disease detection, we will have the opportunity to compare the results of these different functions with a graphical presentation using evaluation metrics, such as accuracy functions and loss functions as Binary Cross-Entropy. The training of the models was carried out using a free accessible database of 20,639 photographs, taken both in the laboratory and in real conditions from the crop fields. The data includes three plant species in fifteen distinct classes of combinations [plant, disease], including some healthy plants.

scholarly journals Transfer learning-based Plant Disease Detection

2021 ◽  
pp. 469-477
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Transfer Learning ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Main Tool ◽  
Classification Problem ◽  
Disease Detection ◽  
Classification Problems ◽  
Crop Species

Deep Neural Networks in the field of Machine Learning (ML) are broadly used for deep learning. Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification problems. Deep neural networks have been highly successful in image classification problems. In this paper, we have shown the use of deep neural networks for plant disease detection, through image classification. This study provides a transfer learning-based solution for detecting multiple diseases in several plant varieties using simple leaf images of healthy and diseased plants taken from PlantVillage dataset. We have addressed a multi-class classification problem in which the models were trained, validated and tested using 11,333 images from 10 different classes containing 2 crop species and 8 diseases. Six different CNN architectures VGG16, InceptionV3, Xception, Resnet50, MobileNet, and DenseNet121 are compared. We found that DenseNet121 achieves best accuracy of 95.48 on test data.

scholarly journals How Convolutional Neural Networks Diagnose Plant Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yosuke Toda ◽  
Fumio Okura
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Disease Diagnosis ◽  
Black Box ◽  
Plant Diseases ◽  
Training Dataset ◽  
Great Success ◽  
Human Decision

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

scholarly journals Meta-Learning for Few-Shot Plant Disease Detection

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2441
Author(s):  
Liangzhe Chen ◽  
Xiaohui Cui ◽  
Wei Li
Keyword(s):  
Neural Networks ◽  
Crop Production ◽  
Plant Disease ◽  
Plant Pathogens ◽  
Feature Matching ◽  
Apple Scab ◽  
Plant Diseases ◽  
Deep Impact ◽  
Meta Learning ◽  
Adaptive Processes

Plant diseases can harm crop growth, and the crop production has a deep impact on food. Although the existing works adopt Convolutional Neural Networks (CNNs) to detect plant diseases such as Apple Scab and Squash Powdery mildew, those methods have limitations as they rely on a large amount of manually labeled data. Collecting enough labeled data is not often the case in practice because: plant pathogens are variable and farm environments make collecting data difficulty. Methods based on deep learning suffer from low accuracy and confidence when facing few-shot samples. In this paper, we propose local feature matching conditional neural adaptive processes (LFM-CNAPS) based on meta-learning that aims at detecting plant diseases of unseen categories with only a few annotated examples, and visualize input regions that are ‘important’ for predictions. To train our network, we contribute Miniplantdisease-Dataset that contains 26 plant species and 60 plant diseases. Comprehensive experiments demonstrate that our proposed LFM-CNAPS method outperforms the existing methods.

Challenges and Issues in Plant Disease Detection Using Deep Learning

2021 ◽  
pp. 56-74
Author(s):  
Priyanka Sahu ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Dinesh Singh ◽  
Ravinder Pal Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Plant Disease ◽  
Image Database ◽  
Plant Diseases ◽  
Disease Detection ◽  
Factors Influencing ◽  
Classification Tasks ◽  
Main Factors

Deep learning (DL) has rapidly become an essential tool for image classification tasks. This technique is now being deployed to the tasks of classifying and detecting plant diseases. The encouraging results achieved with this methodology hide many problems that are rarely addressed in related experiments. This study examines the main factors influencing the efficiency of deep neural networks for plant disease detection. The challenges discussed in the study are based on the literature as well as experiments conducted using an image database, which contains approximately 1,296 leaf images of the beans crop. A pre-trained convolutional neural network, EfficientNet B0, is used for training and testing purposes. This study gives and emphasizes on factors and challenges that may potentially affect the use of DL techniques to detect and classify plant diseases. Some solutions are also suggested that may overcome these problems.

scholarly journals A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification

2021 ◽  
Vol 11 (4) ◽  
pp. 1878
Author(s):  
Zhirui Luo ◽  
Qingqing Li ◽  
Jun Zheng
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Economic Losses ◽  
Significant Delay ◽  
Disease Identification

Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make wrong predictions. Successful adversarial attacks on deep learning (DL)-based plant disease identification systems could result in a significant delay of treatments and huge economic losses. This paper is the first attempt to study adversarial attacks and detection on DL-based plant disease identification. Our results show that adversarial attacks with a small number of perturbations can dramatically degrade the performance of DNN models for plant disease identification. We also find that adversarial attacks can be effectively defended by using adversarial sample detection with an appropriate choice of features. Our work will serve as a basis for developing more robust DNN models for plant disease identification and guiding the defense against adversarial attacks.

scholarly journals Plant disease severity estimation by computer vision methods

2021 ◽  
Vol 51 (2) ◽  
pp. 107-112
Author(s):  
V. G. Altukhov
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Leaf Area ◽  
Disease Severity ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Plant Diseases ◽  
Visual Features ◽  
Lesion Area ◽  
Disease Progress

The first stage results within the framework of the thesis “Investigation of computer vision methods and algorithms in the field of plant diseases detection” are presented. The analysis of the work related to the automatic assessment of plant disease severity was carried out. It was established that for solving problems in this field, convolution neural networks are promising methods, which are currently superior to classical methods of computer vision in terms of accuracy. To assess the severity degree, classification and segmentation architectures of convolutional neural networks are used. Classification architectures are able to take into account disease visual features at different stages of the disease development, but information about the actual affected area is unavailable. On the other hand, solutions based on segmentation architectures provide actual data on the lesion area, but do not grade severity levels according to disease visual features. Based on the result of the research into the application of convolutional neural networks and options for their use, the goal of this study was determined, which is to develop an automatic system capable of determining the lesion area, as well as to take into account disease visual features and the type of immunological reaction of the plant at different stages of disease progress. It is planned to build a system based on the segmentation architecture of a convolutional neural network, which will produce multi-class image segmentation. Such a network is able to divide image pixels into several classes: background, healthy leaf area, affected leaf area. In turn, the class "affected leaf area" will include several subclasses corresponding to the disease visual features at different stages of disease progress.

scholarly journals Deep Learning Model for Plant Disease Detection

2020 ◽  
Vol 9 (1) ◽  
pp. 750-754
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Plant Disease ◽  
Early Stage ◽  
Learning Model ◽  
Plant Diseases ◽  
Convolution Neural Networks ◽  
Deep Learning Model

Food is one of the basic needs of human being. We know that the population is rising enormously.so it is more important to feed such a huge population. But nowadays plants are largely affected with various types of diseases. If proper care should not be taken then it will show effect on quality of food products, quantity and finally on productivity of crops.. so, Early detection of plant disease is very essential, but it is very hard to farmers to monitor the crops manually it takes more processing time, huge amount of work, expensive and need expertised persons. Automatic detection of plant diseases helps the farmers to monitor the large fields easily,because our approach of using convolution neural networks provides a chance to discover diseases at the very early stage. By using Image Processing and machine learning models we can detect the plant diseases automatically but the accuracy is very less, early detection is also a major challenge. With the modern advanced developments in deep learning, in our project we have implemented the convolution neural networks(CNN) which comprises of different layers,by using those layers we can automatically detect and classify the diseases present in the plants. High Classification accuracy and more processing speed are the main advantages of our approach. After training the model on color, grayscale and segmented datasets our deep learning model will be capable of classifying a large number of different diseases and our project gives us the name of the disease that the plant has with its confidence level and also provides remedies for corresponding diseases

Sign in / Sign up

Export Citation Format

Share Document