Automatic Detection of Leaf Disease Using CNN Algorithm

Nandhini, S.; Suganya, R.; Nandhana, K.; Varsha, Saxena; Deivalakshmi, S.; Thangavel, Senthil Kumar

doi:10.1007/978-981-15-7106-0_24

Cited by 17 publications

(4 citation statements)

References 6 publications

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“…Being contrast to pre-pooling strips of size 2*2 with a stride of 2 yielding the same outcome parameters. The continuous pooling feature leads to a 0.4 percent decline in the top-1 failure rate and a 0.3 percent reduction in the top-5 prediction error [54].…”

Section: Gl-cnn Processing Layersmentioning

confidence: 99%

A novel CNN gap layer for growth prediction of palm tree plantlings

Kumar,

Rajmohan,

Adeola Ajagbe

et al. 2023

PLoS ONE

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Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

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Section: Gl-cnn Processing Layersmentioning

confidence: 99%

A novel CNN gap layer for growth prediction of palm tree plantlings

Kumar,

Rajmohan,

Adeola Ajagbe

et al. 2023

PLoS ONE

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“…Nandhini et al [ 32 ] used an unsupervised multi-scale CNN for robust automatic railway tracking for detection. They used vibration data for crack detection.…”

Section: Related Workmentioning

confidence: 99%

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data

Rustam

Ishaq

Hashmi

et al. 2023

Sensors

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Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings.

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“…The suggested CNN architecture achieved 96% classification accuracy. [8] In order to identify and diagnose leaf illnesses, this study uses a convolution neural network to classify photos. The suggested method's primary goal is to use a neural network to treat tomato, corn, and apple leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

An Improved CNN model for Identifying Tomato Leaf Diseases

BHARALI

2024

tjes

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A major loss in gross domestic product, quantity and quality of products produced, as well as tomato production, is caused by diseases in tomato leaves due to which farmers have a difficult time in controlling and monitoring the health of tomato leaves, one of which is leaf disease. In our project, we developed an Enhanced CNN by using data augmentation techniques to identify the seven classes(blight ,leaf curl, leaf miner ,Alteneria, leaf spot, cutwork infected) of tomato leaf diseases. Using 27807 trainable parameters, the enhanced CNN obtains the maximum training accuracy of ninety nine point nine eight percent(99.98%) and validating accuracy of ninety eight point four percent(98.4%) .With fewer parameters, The Enhanced CNN can more accurately determine the type of illness of tomato leaf. Our Enhanced CNN model also determine the type of illness of tomato leaf when tested with the images of diseased tomato leaf collected from the internet sources.Using 152850 trainable parameters the enhanced CNN obtain the maximum training accuracy of 99.68% and validation accuracy of 89%.

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Automatic Detection of Leaf Disease Using CNN Algorithm

Cited by 17 publications

References 6 publications

A novel CNN gap layer for growth prediction of palm tree plantlings

A novel CNN gap layer for growth prediction of palm tree plantlings

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data

An Improved CNN model for Identifying Tomato Leaf Diseases

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