Olive Leaf Disease Identification Framework using Inception V3 Deep Learning

Mamdouh, Nariman; Khattab, Ahmed

doi:10.1109/dts55284.2022.9809893

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…This remarkable performance places it in a favorable position alongside existing research efforts. In [4] and [13], they reported an accuracy of 96%, closely followed by [12], which achieved a commendable accuracy of 95.6%. While [10] attained an accuracy of 95%, the significant lead of the proposed model with an accuracy of 98.3% shows its potential for higher precision in classification tasks.…”

Section: Comperive With Other Studiesmentioning

confidence: 91%

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Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

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Section: Comperive With Other Studiesmentioning

confidence: 91%

“…In [13], the author used the deep learning architecture of Inception V3 to accurately and efficiently classify olive leaf diseases. This framework addresses the critical challenge of identifying diseases affecting olive leaves by leveraging the capabilities of a robust convolutional network architecture.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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“…The works of [11,36] focus on the detection of bark beetle damage on trees using customized CNNs on multispectral imagery. There are several studies using ResNet variants [18,19,37], VGG16 [37,38], and Inception-v3 [39] models on different forestry-related tasks with good results; therefore, these three models have also been evaluated in this work.…”

Section: Tree Health Classification From Multispectral Uav Data Using...mentioning

confidence: 99%

Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest

Anwander,

Brandmeier,

Paczkowski

et al. 2024

Remote Sensing

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We present an evaluation of different deep learning and machine learning approaches for tree health classification in the Black Forest, the Harz Mountains, and the Göttinger Forest on a unique, highly accurate tree-level dataset. The multispectral UAV data were collected from eight forest plots with diverse tree species, mostly conifers. As ground truth data (GTD), nearly 1500 tree polygons with related attribute information on the health status of the trees were used. This data were collected during extensive fieldwork using a mobile application and subsequent individual tree segmentation. Extensive preprocessing included normalization, NDVI calculations, data augmentation to deal with the underrepresented classes, and splitting the data into training, validation, and test sets. We conducted several experiments using a classical machine learning approach (random forests), as well as different convolutional neural networks (CNNs)—ResNet50, ResNet101, VGG16, and Inception-v3—on different datasets and classes to evaluate the potential of these algorithms for tree health classification. Our first experiment was a binary classifier of healthy and damaged trees, which did not consider the degree of damage or tree species. The best results of a 0.99 test accuracy and an F1 score of 0.99 were obtained with ResNet50 on four band composites using the red, green, blue, and infrared bands (RGBI images), while VGG16 had the worst performance, with an F1 score of only 0.78. In a second experiment, we also distinguished between coniferous and deciduous trees. The F1 scores ranged from 0.62 to 0.99, with the highest results obtained using ResNet101 on derived vegetation indices using the red edge band of the camera (NDVIre images). Finally, in a third experiment, we aimed at evaluating the degree of damage: healthy, slightly damaged, and medium or heavily damaged trees. Again, ResNet101 had the best performance, this time on RGBI images with a test accuracy of 0.98 and an average F1 score of 0.97. These results highlight the potential of CNNs to handle high-resolution multispectral UAV data for the early detection of damaged trees when good training data are available.

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Olive Leaf Disease Identification Framework using Inception V3 Deep Learning

Cited by 2 publications

References 12 publications

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest

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