Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks

Zhang, Xihai; Qiao, Yue; Meng, Fanman; Fan, Chengguo; Zhang, Mingming

doi:10.1109/access.2018.2844405

Cited by 498 publications

(182 citation statements)

References 17 publications

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“…In contrast, CNNs learn data characteristics from convolution operations, which is better suited for pattern recognition of images. Consequently, CNNs have been used to detect and diagnose plant diseases [30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

2019

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The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.

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Section: Introductionmentioning

confidence: 99%

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

2019

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“…A number of recent studies have successfully used machine learning approaches, particularly CNNs, to detect plant diseases in images that can be acquired without a specialized camera (standard red–green–blue or RGB photos). Many studies have relied on the expert‐curated set of over 50,000 plant disease images released through PlantVillage (described by Hughes and Salathe, 2016; utilized by Mohanty et al, 2016; Islam et al, 2017; Wang et al, 2017; Barbedo 2018; Ferentinos, 2018; Too et al, 2019; Zhang et al, 2018). Due to its size and scope, this dataset has become a benchmark for new approaches in plant disease detection, analogous to the large datasets such as COCO (Lin et al, 2014) or ImageNet (Deng et al, 2009) used for many other computer vision tasks.…”

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confidence: 99%

Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery

Wiesner-Hanks

Stewart

et al. 2019

The Plant Phenome Journal

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“…Zhang et al [24] have proposed two improved profound convolutional neural systems model to recognizing nine kinds of maize leaves. GoogLeNet and Cifar10 utilized accomplish high recognizable proof precision, 98.9% and 98.8%, individually.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

Keskar,

Maktedar

2019

IJEAT

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Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses around the automatic identification proof of therapeutic plants that are regularly utilized in Ayurveda. The fundamental highlights required to distinguish a medicinal plant is its leaf shape, color and texture. In this paper, we propose efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques. In EAC-AMP, image corners detect first and top, bottom leaf edges are computed by the improved edge detection algorithm. After preprocessing, the segmentation can achieve using spider optimization neural network (SONN), which segments leaf regions from an image. The time and frequency domain features are computed by the symbolic accurate approximation (SAX); other features shape features, color features and tooth features are computed by the two-dimensional binary phase encoding (2DBPE). Finally, a whale optimization with deep neural network (DNN) classifier is used to characterize the type of plants. Accuracy in identification of any ayurvedic plant leaf is achieved by understanding and extracting the plant features. The main objective of the proposed EAC-AMP approach is to increase the accuracy of classifier. MATLAB experimental analysis showed better results such as accuracy, sensitivity and specificity.

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Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks

Cited by 498 publications

References 17 publications

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery

Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

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