Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network

Dong, Mengping; Mu, Shaomin; Shi, Aijun; Mu, Wenqian; Sun, Wenjie

doi:10.25165/j.ijabe.20201304.4826

Cited by 7 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deep learning model Differential Amplification Convolutional Neural Network (DACNN) proposed to recognize these diseases by combined capabilities of four classifiers Support Vector Machine, Random forest, Softmax and K-Nearest Neighbor (KNN). The proposed model accuracy is 95.18% which is highest among Inception v3, ZFNet, LeNet and Alexnet models [76]. The diagnosis and recognition of the leaves diseases portray the status of the crop.…”

Section: Wheatmentioning

confidence: 88%

Detection and Classification of Grain Crops and Legumes Disease: A Survey

Urva¹

2021

STAIQC

View full text Add to dashboard Cite

The agriculture plays very important role as it helps to accomplish the need of food among people. The production in agriculture consequentially contributes to the economy of every country. The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. The monitoring of crops health and identification of diseases at early days is very challenging and emerging task in agriculture. So, it is very important to prevent crops from fatal diseases in the early stage, but the manual process of disease discovery can lead to erroneous magnitude of pesticides. The trouble is figure out by automate discovery of diseases and supplication of relevant medication on time. It is very necessary to find out accurate disease to overcome heavy loss to economy. From the few decades, to detect disease correctly, the process of detection become automate using emerging technologies and techniques using computer vision, machine learning and image processing. This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. The studies addresses that there is still many limitations and challenges find in different phases in plant disease detection system. The presented research also highlights the pros and cons of different techniques that help out the researchers for contribution in future.

show abstract

Section: Wheatmentioning

confidence: 88%

Detection and Classification of Grain Crops and Legumes Disease: A Survey

Urva¹

2021

STAIQC

View full text Add to dashboard Cite

show abstract

“…In a number of real-world applications, the isotropic assumption is false and does not accurately reflect the probable connection between the sample's dimensional components. [13][14][15][16] As a result of the growth of artificial intelligence [14], image processing and classification research makes substantial use of deep learning techniques [15]. Dong et al [14] suggested use a convolutionl neural network with differential amplification to identify wheat diseases.…”

Section: Literature Surveymentioning

confidence: 99%

“…[13][14][15][16] As a result of the growth of artificial intelligence [14], image processing and classification research makes substantial use of deep learning techniques [15]. Dong et al [14] suggested use a convolutionl neural network with differential amplification to identify wheat diseases. Its average identification accuracy increased by 6.03 percent compared to LeNet-5.…”

Section: Literature Surveymentioning

confidence: 99%

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

Kanna.R¹,

V.²

2023

INDJCSE

View full text Add to dashboard Cite

Crop diseases and insect pests are the most significant obstacles to agricultural production. Consequences include a decrease in crop yields and the obstruction of the long-term development of high-quality, high-efficiency agriculture. Rapid and accurate detection of wheat leaf disease categories promotes early deployment of field control and improvement of wheat yield and quality. It's no exaggeration to say that wheat is one of the world's most vital crops. Wheat leaf diseases, though, severely hamper development. The quality of wheat and the agricultural economy rely on prompt and correct identification of wheat leaf diseases. This work propose an integrated machine learning technique, According to the results of this study, when compared to other models, the Fuzzy Opponent Histogram Filter that makes use of Bayes Net performs the best. The Fuzzy Opponent Histogram Filter with Bayes Net gives best outcome which is 97% of accuracy.

show abstract

“…Therefore, machine learning techniques achieve low classification accuracy as compared to deep learning techniques for wheat rust disease identification [14], [15]. hybrid models have been developed (CNN-LSVM [16] and Differential amplification CNN (DACNN) [17] for wheat rust disease identification. After the classification of crop diseases [18], [19] different severity levels of diseases are identified at a large scale.…”

Section: Introductionmentioning

confidence: 99%

Combined CNN with STARGAN for Wheat Yellow Rust Disease Classification

Kumar¹,

Kukreja²

2023

IJCDS

View full text Add to dashboard Cite

Experienced evaluators takes a lot of time for the correct prediction of wheat yellow rust. For better prediction of wheat yellow rust disease in wheat plant, computer assisted techniques such as machine learning (ML), deep learning (DL), image processing and computer vision techniques are employed. While considering these aspects, an automatic classification system for wheat yellow rust using a hybrid approach of a generative adversarial network (GAN) and convolutional neural network (CNN) is proposed. Also, the proposed model helps to identify wheat yellow rust disease at different severity levels (very low, low, medium, high, and very high). For achieving objectives about proposed model, State of Art GAN (STARGAN) helps in data augmentation of wheat plant disease images. Finally, a wheat rust model based on convolutional networks was trained on generated data. A case study has also been accomplished on the generated data by deploying a CNN model for classification. Then a comparative analysis of the proposed methodology has been consummated by differentiating the performance metric with the fully convolutional network, deep CNN (DCNN) and random forest models. A Deep CNN is a CNN model with a huge number of hidden layers. Comparing the proposed approach with other models, the proposed approach achieves a greater classification accuracy of 95.6% at a medium severity level.

show abstract

Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network

Cited by 7 publications

References 14 publications

Detection and Classification of Grain Crops and Legumes Disease: A Survey

Detection and Classification of Grain Crops and Legumes Disease: A Survey

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

Combined CNN with STARGAN for Wheat Yellow Rust Disease Classification

Contact Info

Product

Resources

About