A Convolution Neural Network based approach to detect the disease in Corn Crop

Agarwal, Mohit; Bohat, Vijay Kumar; Ansari, Mohd Dilshad; Sinha, Anubhav; Gupta, Suneet Kr.; Garg, Deepak

doi:10.1109/iacc48062.2019.8971602

Cited by 50 publications

(18 citation statements)

References 11 publications

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“…Methodology [8] Transfer learning (GoogleNet), data augmentation [12] Bayesian optimization, data augmentation [13] Developed a CNN [14] Developed CNN based on DenseNet [15] Multi-context fusion for contextual and visual features [16] PLS fusion applied on the 6th and 7th layer of VGG19…”

Section: Related Workmentioning

confidence: 99%

“…In [13], the authors developed a CNN consisting of three convolution layers, three max-pooling layers, and two fully connected layers. The authors used a subset of the PlanVillage dataset containing corn leaves with three diseases: corn gray leaf spot, corn common rust, corn northern leaf blight, and a healthy class on which the developed model achieved a classification accuracy of 94%.…”

Section: Related Workmentioning

confidence: 99%

“…This work proposes a model that uses two different CNNs and uses feature fusion techniques to increase its predictive power as opposed to work in [8], [12], [13], and [14] that uses the features extracted by a single CNN. In comparison, this work utilizes features from two different CNNs.…”

Section: Related Workmentioning

confidence: 99%

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End-to-End Deep Learning Model for Corn Leaf Disease Classification

et al. 2022

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Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.INDEX TERMS Colutional neural networks, deep learning, deep features, feature fusion, plant disease.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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End-to-End Deep Learning Model for Corn Leaf Disease Classification

et al. 2022

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“…Performance of each optimizer was displayed using plots and a deep analysis has been done on Adagrad optimizer. Lastly, an improvised Adagrad optimizer was developed to get better results, a certain improvement was observed for each optimizer, and optimizers were developed to improve the accuracy as well as the performance of the Model [10][11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

A Performance Comparison of Optimization Algorithms on a Generated Dataset

Gaddam

Ansari

Vuppala

et al. 2021

Lecture Notes in Electrical Engineering

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Optimization Algorithms are one of the important machine learning techniques, they play a vital role in improving the performance of the model. In supervised learning, data samples are given, to train a model whose outcomes are already known. The dataset of 5000 samples with 2 features and 4 classes have been generated. We've trained the dataset with different Optimization algorithms such as Gradient descent, Mini Batch gradient descent, Momentum, NAG, RMSprop, Adagrad and Adam.To check the performance, these algorithms have been employed on the proposed model and compared the accuracy obtained by each algorithm. The presented work provides a clear understanding as well as the working of algorithms. The proposed model effectiveness can be seen by comparing different optimization algorithms. The developed model is able to obtain the maximum accuracy of 0.926 on Adam at testing phase with a learning rate of 0.5. The test result shows that Adam performs better than the other algorithms.

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“…Widely applicable in modelling temporal patterns in activity recognition such as progressive detection of activity levels, fall detection and heart attack in elderly people. In deep learning based human action recognition, the most commonly used commonly used function is softmax, hence we also use the same during training as it handles the probabilities of the prediction of actions in multi-class classification (Nweke et al, 2018;Agarwal et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Residual LSTM-based Human Activity Recognition

Malki¹,

Atlam²,

Dagnew³

et al. 2020

CIS

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The Residual Long Short Term Memory (LSTM) deep learning approach is attracting attension of many researchers due to its efficiency when trained on high dimensional datasets. Nowadays, Human Activity Recognition (HAR) has come with enormous challenges that have to be addressed. In addressing such a problem, one can think of developing an application that can help the elderly people as an assistant when it works in collaboration with other timely technologies such as wearable devices with the help of IoT. Many research works are using a standard dataset in evaluating their proposed method in this regard. The dataset comes with its own challenge such as imbalanced classes. In this work, we propose to apply different machine learning techniques to address the specified problems and the method is validated on a standard dataset. To validate the proposed method, we evaluated using different standard metrics such as classification accuracy, precision, recall, f1-score, and Receiver Operating Characteristic (ROC) curve. The proposed method achieves an Area Under Curve (AUC) of 100%, 97.66% of accuracy, 91.59% precision,  93.75% of recall and 92.66% of F1-score respectively.

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A Convolution Neural Network based approach to detect the disease in Corn Crop

Cited by 50 publications

References 11 publications

End-to-End Deep Learning Model for Corn Leaf Disease Classification

End-to-End Deep Learning Model for Corn Leaf Disease Classification

A Performance Comparison of Optimization Algorithms on a Generated Dataset

Bidirectional Residual LSTM-based Human Activity Recognition

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