scholarly journals Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Srdjan Sladojevic ◽  
Marko Arsenovic ◽  
Andras Anderla ◽  
Dubravko Culibrk ◽  
Darko Stefanovic
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Plant Disease ◽  
Plant Diseases ◽  
New Approach ◽  
Recognition Model ◽  
Convolutional Networks ◽  
Learning Framework ◽  
First Time ◽  
Leaf Image

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

scholarly journals Analysis and Implementation of Fruit/Leave Disease Detection using Image Processing and Neural Approach

2021 ◽  
Vol 7 (09) ◽  
pp. 186-194
Author(s):  
Krishna Madheshiya, Prashant Richhariya and Dr. Anita Soni
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Detection ◽  
New Approach ◽  
Detection Model ◽  
Learning Framework ◽  
Deep Cnn ◽  
First Time ◽  
Leaf Image

The latest generation of convolution neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of fruit/plant disease detection model, based on leaf image processing and classification, by the use of ANN. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training

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.

scholarly journals Plant disease prediction using convolutional neural network

2021 ◽  
Vol 9 (2) ◽  
pp. 283-293
Author(s):  
Hema M S ◽  
†, Niteesha Sharma ◽  
Y Sowjanya ◽  
Ch. Santoshini ◽  
R Sri Durga ◽  
...  
Keyword(s):  
Plant Disease ◽  
Plant Diseases ◽  
Disease Classification ◽  
Dense Layer ◽  
Convolutional Networks ◽  
Soil Features ◽  
Proposed Model ◽  
Plant Image ◽  
Manual Examination ◽  
Processing Steps

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared.  The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity

A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures

2015 ◽  
Vol 763 ◽  
pp. 175-181
Author(s):  
Simone Silva Frutuoso Souza ◽  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Approach ◽  
Inspection Process ◽  
Artificial Neural ◽  
Aluminum Beam ◽  
First Time ◽  
Hybrid Methodology

In this paper presents a new hybrid methodology to perform fault detection and classification of aircraft structures using the tool as ARTMAP-Fuzzyand Perceptron multi-layer artificial neural networks. This method is divided into two steps, the first step performed by the multi-layer Perceptron neural network, which consists in the detection of abnormalities in the structure. The second step is performed by ARTMAP-Fuzzyneural network and consists of the classification of faults structural detected in the first time. The main application of this hybrid methodology is to assist in the inspection process of aeronautical structures in order to identify and characterize flaws as well, make decision-making in order to avoid accidents or air crashes. To evaluate this method, the modeling and simulation was carried out signals from a numerical model of an aluminum beam. The results obtained by the methodology demonstrating robustness and accuracy structural flaws.

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 Plant Disease Prediction System using Image Processing

2020 ◽  
pp. 162-167
Author(s):  
B Uma Jagadeswari ◽  
D Harshitha ◽  
G Vineela ◽  
B Siri ◽  
Yaragani Sowmya
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Negative Impact ◽  
Agricultural Productivity ◽  
Significant Loss ◽  
Plant Diseases ◽  
Disease Prediction ◽  
Agricultural Field ◽  
Processing Techniques ◽  
Leaf Image

Agriculture productivity is the major issue which affects the Indian economy. Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. The major cause for decrease in the quality and amount of agricultural productivity is due to the diseases in plants. The occurrence of diseases in plants may result in significant loss in both quality and quantity of agricultural productivity. This can produce the negative impact on the countries whose economies are primarily dependent on the agriculture. Farmers encounter great difficulties in detecting and controlling plant diseases. Hence the detection of plant diseases in the earlier stages is very important to avoid the loss in terms of quality, quantity and finance. This paper mainly focuses on the approach based on image processing techniques that help farmers for detecting the diseases of plants by uploading leaf image to the system.

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.

scholarly journals Capsule Networks: An Alternative Approach to Image Classification Using Convolutional Neural Networks

2020 ◽  
Author(s):  
KARAN SOOD ◽  
Jinan Fiaidhi
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
New Approach ◽  
Alternative Approach

I studied a new approach called Capsule networks to overcome the drawbacks of convolutional neural networks.

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