Identification of plant leaf diseases using a nine-layer deep convolutional neural network

Geetharamani, G.; Pandian, J. Arun

doi:10.1016/j.compeleceng.2019.04.011

Cited by 482 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, using CNNs to identify early plant diseases has become a research focus of agricultural informatization. In (Mohanty et al, 2016;Zhang and Wang, 2016;Lu J. et al, 2017;Lu Y. et al, 2017;Khan et al, 2018;Liu et al, 2018;Geetharamani and Pandian, 2019;Ji et al, 2019;Jiang et al, 2019;Liang et al, 2019;Oppenheim et al, 2019;Pu et al, 2019;Ramcharan et al, 2019;Wagh et al, 2019;Zhang et al, 2019a;Zhang et al, 2019b;), CNNs are extensively studied and applied to the diagnosis of plant diseases. According to these studies, CNNs can learn advanced robust features of diseases directly from original images rather than selecting or extracting features manually, which outperform the traditional feature extraction approaches.…”

Section: Introductionmentioning

confidence: 99%

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Anthracnose, brown spot, mites, black rot, downy mildew, and leaf blight are six common grape leaf pests and diseases, which cause severe economic losses to the grape industry. Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. This paper proposes a novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases. First, based on 4,023 images collected in the field and 3,646 images collected from public data sets, a data set of 107,366 grape leaf images is generated via image enhancement techniques. Afterward, Inception structure is applied for strengthening the performance of multidimensional feature extraction. In addition, a dense connectivity strategy is introduced to encourage feature reuse and strengthen feature propagation. Ultimately, a novel CNNbased model, namely, DICNN, is built and trained from scratch. It realizes an overall accuracy of 97.22% under the hold-out test set. Compared to GoogLeNet and ResNet-34, the recognition accuracy increases by 2.97% and 2.55%, respectively. The experimental results demonstrate that the proposed model can efficiently recognize grape leaf diseases. Meanwhile, this study explores a new approach for the rapid and accurate diagnosis of plant diseases that establishes a theoretical foundation for the application of deep learning in the field of agricultural information.

show abstract

Section: Introductionmentioning

confidence: 99%

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…They experimented on various batch size, epoch, validation folds, and dropout factorization index on CNN. They compared the efficiency of their proposal with popular CNN architectures including AlexNet, VggNet, Inception-v3 and ResNet and reported the superiority of their proposal with rates of 93.00%, 92.00%, and 93.00% for pepper leaf disease identification accuracy, sensitivity, and specificity, respectively [18]. Kurup et al applied conventional CapsNet architecture to the PlantVillage dataset for leaf classification and leaf disease identification tasks.…”

Section: Discussionmentioning

confidence: 99%

“…They iterated their model using different dropout factorization, learning rate, and fully-connected layers. Their most light-weight architecture for plant leaf disease classification was established a CNN with nine layers [18].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

Altan

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

Deep Learning (DL) is a high capable machine learning algorithm which composed the advanced image processing as feature learning and supervised learning with detailed models with many hidden layers and neurons. DL demonstrated its efficiency and robustness in many big data problems, computer vision, and more. Whereas it has an increasing popularity day by day, it has still some deficiencies to construe the relationship between learned feature maps and spatial information. Capsule network (CapsNET) is proposed to overcome the shortcoming by excluding the pooling layer from the architecture and transferring spatial information between layers by capsule. In this paper, CapsNET architecture was proposed to evaluate the performance of the model on classification of plant leaf diseases using simple reduced capsules on leaf images. Plant leaf diseases are common and prevalent diseases that disrupt harvesting and yielding for agriculture. CapsNET has capability of detailed analysis for even small stains that may lead seed dressing time and duration. The proposed CapsNET model aimed at assessing the applicability of various feature learning models and enhancing the learning capacity of the DL models for bell pepper plants. The healthy and diseased leaf images were fed into the CapsNET. The proposed CapsNET model reached high classification performance rates of 95.76%, 96.37%, and 97.49% for accuracy, sensitivity, and specificity, respectively.

show abstract

“…It showed an accuracy ranging from 70% to 98% for different models. Geetharamani et al [17] designed a system by considering nine layer deep convolutional neural network. The architecture was taught using 39 different classes from an open dataset of plant leaf images.…”

Section: Techniques Based On Deep Learningmentioning

confidence: 99%

A Comparative Analysis on AI Techniques for Grape Leaf Disease Recognition

Pai¹,

Thomas²

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Grape or Grapevine (Vitis Vinifera) belonging to the Vitaceae family is one of India's most commercially important fruit crops. It is a widely temperate crop that has become accustomed to the subtropical climate of the peninsula of India. In a Grape vineyard, there are more chances of grape fruits and its leaf to confront with diseases. Manual Observation is not feasible and is also time constrained for experts and agronomists to track with. Inorder to predict the disease in early stage, we deal with a literature survey on different methods pertinent to disease identification and classification. Through this survey, we find out the best method to be followed for disease tracking. The composition at first, presents a detailed terminology related to different kind of grape leaf diseases. Furthermore, a survey regarding automated grape leaf disease identification and categorization methods are carried out, which deals with techniques like machine learning and deep learning.

show abstract

Identification of plant leaf diseases using a nine-layer deep convolutional neural network

Cited by 482 publications

References 21 publications

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

A Comparative Analysis on AI Techniques for Grape Leaf Disease Recognition

Contact Info

Product

Resources

About