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

Sladojević, Srdjan; Arsenović, Marko; Anderla, Andraš; Ćulibrk, Dubravko; Stefanović, Darko

doi:10.1155/2016/3289801

Cited by 1,289 publications

(578 citation statements)

References 32 publications

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“…The experimental results indicate that a CNN-based model can automatically extract the requisite classification features and obtain the optimal performance. In [14], Sladojevic et al proposed a novel approach based on deep convolutional networks to detect plant disease. By discriminating the plant leaves from their surroundings, 13 common different types of plant diseases were recognized by the proposed CNN-based model.…”

Section: Related Workmentioning

confidence: 99%

“…Appropriate datasets are required at all stages of object recognition research, from training the CNN-based models to evaluating the performance of the recognition algorithms [14].…”

Section: Apple Leaf Pathological Image Acquisitionmentioning

confidence: 99%

“…Inspired by the breakthrough of the convolutional neutral network in image-based recognition, the use of convolutional neural networks to identify early disease images has become a new research hotspot in agricultural informatization. In [13][14][15][16][17][18][19][20], convolutional neural networks (CNNs) are widely studied and used in the field of crop disease recognition. These studies show that convolutional neural networks have not only reduced the demand of image preprocessing, but also improved the recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

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Section: Related Workmentioning

confidence: 99%

“…Appropriate datasets are required at all stages of object recognition research, from training the CNN-based models to evaluating the performance of the recognition algorithms [14].…”

Section: Apple Leaf Pathological Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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“…Recently, while the probability statistical models perform well in many fields, deep neural networks as a new wave tide in machine learning, have achieved great performances in many domains such as image classification [10], knowledge discovery [11] and translation [12] etc. Collobert et al [13] propose a unified neural network architecture and learning algorithm to do various NLP tasks and also achieved a better result for NER task.…”

Section: Related Workmentioning

confidence: 99%

Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition

Sun

Xie

et al. 2017

Database Systems for Advanced Applications

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Abstract. Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further.

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“…Their study focuses on feature extraction phases. S Sladojevic et al study plant disease classification based on digital leaf image using deep learning algorithm [4]. Plant leaf has special character in texture, shape, and color features, and it challenges in digital image processing and pattern recognition study, how to get those feature automatically [5].…”

Section: Introductionmentioning

confidence: 99%

Plant Leaf Recognition Using Competitive Based Learning Algorithm

Imah

Rahayu

Wintarti

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Plant recognition based on digital leaf image has received as particular attention in computer vision and intelligence system, due its important implication in automatic plant identification. Plant species have the unique leaf characteristics such as the shape, texture, margin, and colour, which different each other. This study presents a novel method for automation plant recognition using Generalized Relevance Learning Vector Quantization (GRLVQ). GRLVQ is a competitive based learning algorithm which is integrating features extraction and classification phases. The experimental result shows that GRLVQ has better performance than the predecessor algorithm.

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

Cited by 1,289 publications

References 32 publications

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition

Plant Leaf Recognition Using Competitive Based Learning Algorithm

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