Leaf and spike wheat disease detection &amp; classification using an improved deep convolutional architecture

Goyal, Lakshay; Sharma, Chandra Mani; Singh, Anupam; Singh, Pradeep

doi:10.1016/j.imu.2021.100642

Cited by 90 publications

(22 citation statements)

References 21 publications

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“…The model is used in an edge environment with Amazon IoT Core to automate disease detection in CXR images in three categories: pneumonia, COVID-19, and normal. The proposed system is good to be used as an assistive tool for the automated screening tool for COVID-19 and viral pneumonia [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

“…SVM was chosen because it employs the kernel method to transform low-dimensional input space to high-dimensional input space, thereby converting a nonseparable representation into a separable one [ 40 ]. Unlike the convolution-based models (ConvNets), where feature extraction is part of an end-to-end process [ 41 ], SVM relies on the need for a feature extraction step. The SVM classifier uses a hyperplane to linearly separate the data using the linear kernel.…”

Section: Comparison With Machine Learning Modelsmentioning

confidence: 99%

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Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma

Goyal²,

Chariar

et al. 2022

Journal of Healthcare Engineering

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Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison With Machine Learning Modelsmentioning

confidence: 99%

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma

Goyal²,

Chariar

et al. 2022

Journal of Healthcare Engineering

Self Cite

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“…PRI and ARI at different development stages were computed as all conceivable three-band combinations across such susceptible wavelengths, and their ability to predict yellow rust disease severity was examined by the author [9]. In the article [10], the author used a better deep convolutional architecture to find and classify leaf and spike wheat diseases. The study comes up with a brand-new way to group wheat diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Wheat Diseases Detection and Classification using Convolutional Neural Network (CNN)

Hossen¹,

Mohibullah²,

Muzammel³

et al. 2022

IJACSA

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Ever since the medieval era, the preponderance of our concentration has been concentrated upon agriculture, which is typically recognized to be one of the vital aspects of the economy in contemporary society. This focus on agriculture can be traced back to the advent of the industrial revolution. Wheat is still another type of grain that, in the same way as other types of harvests, satisfies the necessity for the essential nutrients that are required for our bodies to perform their functions correctly. On the other hand, the supply of this harvest is being limited by a variety of rather frequent ailments. This is making it difficult to meet demand. The vast majority of people who work in agriculture are illiterate, which hinders them from being able to take appropriate preventative measures whenever they are necessary to do so. As a direct consequence of this factor, there has been a reduction in the total amount of wheat that has been produced. It can be quite difficult to diagnose wheat illnesses in their early stages because there are so many various forms of environmental variables and other factors. This is because there are numerous distinct sorts of agricultural products, illiteracy of agricultural workers, and other factors. In the past, a variety of distinct models have been proposed as potential solutions for identifying illnesses in wheat harvests. This study demonstrates a two-dimensional CNN model that can identify and categorize diseases that affect wheat harvests. To identify significant aspects of the photos, the software employs models that have previously undergone training. The suggested method can then identify and categorize disease-affected wheat crops as distinct from healthy wheat crops by employing the major criteria described above. The reliability of the findings was assessed to be 98.84 percent after the collection of a total of 4800 images for this study. These images included eleven image classes of images depicting diseased crops and one image class of images depicting healthy crops. To offer the suggested model the capability to identify and classify diseases from a variety of angles, the photographs that help compensate for the collection were flipped at a variety of different perspectives. These findings provide evidence that CNN can be applied to increase the precision with which diseases in wheat crops are identified.

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“…3 Early detection and classification of agricultural illnesses have been accomplished using machine learning approaches. [4][5][6][7] Despite their high accuracy, conventional machine learning techniques require calibration. 8 Decision-makers are typically worried about projections and the degree of confidence in such predictions.…”

Section: Introductionmentioning

confidence: 99%

“…Farmers in remote areas may be able to monitor their crops without the help of specialists if agricultural disease detection and classification are automated, which could increase the precision of expert disease diagnosis 3 . Early detection and classification of agricultural illnesses have been accomplished using machine learning approaches 4–7 . Despite their high accuracy, conventional machine learning techniques require calibration 8 .…”

Section: Introductionmentioning

confidence: 99%

Classification of cassava leaf diseases using deep Gaussian transfer learning model

Ahishakiye

Mwangi

Murithi

et al. 2023

Engineering Reports

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In Sub‐Saharan Africa, experts visually examine the plants and look for disease symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine learning algorithms have been employed to quickly identify and classify crop diseases. In this study, we propose a model that integrates a transfer learning approach with a deep Gaussian convolutional neural network model. In this study, two pre‐trained transfer learning models were used, that is, MobileNet V2 and VGG16, together with three different kernels: a hybrid kernel (a product of a squared exponential kernel and a rational quadratic kernel), a squared exponential kernel, and a rational quadratic kernel. In experiments using MobileNet V2 and the three kernels, the hybrid kernel performed better, with an accuracy of 90.11%, compared to 86.03% and 85.14% for the squared exponential kernel and a rational quadratic kernel, respectively. Additionally, experiments using VGG16 and the three kernels showed that the hybrid kernel performed better, with an accuracy of 88.63%, compared to the squared exponential kernel's accuracy of 84.62% and the rational quadratic kernel's accuracy of 83.95%, respectively. All the experiments were done using a traditional computer with no access to GPU and this was the major limitation of the study.

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Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture

Cited by 90 publications

References 21 publications

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Wheat Diseases Detection and Classification using Convolutional Neural Network (CNN)

Classification of cassava leaf diseases using deep Gaussian transfer learning model

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