Symptom based automated detection of citrus diseases using color histogram and textural descriptors

Ali, Hussam; Lali, M. Ikram Ullah; Nawaz, Muhammad; Sharif, Muhammad; Saleem, Basharat Ali

doi:10.1016/j.compag.2017.04.008

Cited by 190 publications

(89 citation statements)

References 21 publications

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“…In this study, the proposed ROI-aware DCNN was implemented using Matlab and trained with four Titan-XP GPUs on a Windows operating system. To compare the proposed method, state-of-the-art methods, i.e., FVE with SIFT [9,10], Clusteringbased feature extraction [23], TL methods using VGG and ResNet [16][17][18], DCNNbased bilinear model [26], and MDFEP [24,25] were tested. Correct recognition accuracy, defined as the ratio of the correctly classified images to the total images, was used for performance evaluation.…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile, the proposed method is trainable in an end-to-end manner. In addition, the used segmentation method in [23] can be applied to simple leaf images where background colors are nearly solid. Precisely, the used approach is closer to the color-based clustering algorithm to select predefined colors, thus implying that region boundaries are not provided.…”

Section: Contributionsmentioning

confidence: 99%

“…In contrast, the proposed method can predict the ROI feature map. Further, this study considers a real environment to some degree in that the leaf images are more complicated in the background than those tested in [23], where the background colors are nearly solid.  Unlike conventional transfer-learning-based methods [16][17][18] that already use the existing architectures, the proposed method presents a new ROI-aware DCNN architecture.…”

Section: Contributionsmentioning

confidence: 99%

“…For leaf disease identification, it is important to find the location of leaf diseases. In [23], color-based clustering algorithm is used to define leaf disease colors. More specifically, spot areas with leaf diseases are manually marked and subsequently averaged to define the leaf disease color.…”

Section: Clustering-based Feature Extractionmentioning

confidence: 99%

“…Fig. 1 shows the procedure of the clustering-based feature extraction for leaf disease identification [23]. This approach can work efficiently if the leaf disease colors are significantly different from the leaf colors and background.…”

Section: Clustering-based Feature Extractionmentioning

confidence: 99%

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Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

Son

Lee³

2020

jist

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A new method of recognizing apple leaf diseases through region-ofinterest-aware deep convolutional neural network is proposed in this paper. The primary idea is that leaf disease symptoms appear in the leaf area whereas the background region contains no useful information regarding leaf diseases. To realize this idea, two subnetworks are first designed. One is for the division of the input image into three areas: background, leaf area, and spot area indicating the leaf diseases, which is the region of interest (ROI), and the other is for the classification of leaf diseases. The two subnetworks exhibit the architecture types of an encoder-decoder network and VGG network, respectively; subsequently, they are trained separately through transfer learning with a new training set containing class information, according to the types of leaf diseases and the ground truth images where the background, leaf area, and spot area are separated. Next, to connect these subnetworks and subsequently train the connected whole network in an end-to-end manner, the predicted ROI feature map is stacked on the top of the input image through a fusion layer, and subsequently fed into the subnetwork used for the leaf disease identification. The experimental results indicate that correct recognition accuracy can be increased using the predicted ROI feature map. It is also shown that the proposed method obtains better performance than the conventional state-of-the-art methods: transfer-learning-based methods, bilinear model, and multiscale-based deep feature extraction and pooling approach.

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Section: Methodsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Clustering-based Feature Extractionmentioning

confidence: 99%

Section: Clustering-based Feature Extractionmentioning

confidence: 99%

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Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

Son

Lee³

2020

jist

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Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm

Khan

Alhaisoni

et al. 2022

Int J Imaging Syst Tech

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In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.

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Intelligent microscopic approach for identification and recognition of citrus deformities

Safdar

Khan

Shah

et al. 2019

Microscopy Res & Technique

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Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial‐based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self‐collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.

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Symptom based automated detection of citrus diseases using color histogram and textural descriptors

Cited by 190 publications

References 21 publications

Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm

Intelligent microscopic approach for identification and recognition of citrus deformities

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