Background suppressing Gabor energy filtering

Cruz, Albert C.; Bhanu, Bir; Thakoor, Ninad

doi:10.1016/j.patrec.2014.10.001

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The feature vectors are z -scored based on the training data before the machine learning step. We compare three feature vector representations: an improved Gabor filter Cruz et al (2015) , Uniform Local Binary Patterns ( Almaev and Valstar, 2013 ), and SIFT features Liu et al (2015) . These features were selected because they provide a good representation of features used currently in literature.…”

Section: Methodsmentioning

confidence: 99%

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

et al. 2017

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We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost.

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Section: Methodsmentioning

confidence: 99%

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

et al. 2017

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“…This singularity in measurements is a result of efficient artificial augmentation, balanced number of class images, optimal parameters assignment, smart network configuration, and usefulness of transfer learning. The problems arising in most traditional attempts [25][26][27][28] to detect plant diseases follow from handengineered features, image enhancement techniques, and labor-intensive methodologies. These traditional attempts are lean to either a small number images in a class or a limited variety of classes of crops.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Deep Learning Model for Olive Diseases Detection

Alruwaili¹,

Alanazi²,

El-Ghany³

et al. 2019

IJACSA

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Worldwide, plant diseases adversely influence both the quality and quantity of crop production. Thus, the early detection of such diseases proves efficient in enhancing the crop quality and reducing the production loss. However, the detection of plant diseases either via the farmers' naked eyes or their traditional tools or even within laboratories is still an error prone and time consuming process. The current paper presents a Deep Learning (DL) model with a view to developing an efficient detector of olive diseases. The proposed model is distinguishable from others in a number of novelties. It utilizes an efficient parameterized transfer learning model, a smart data augmentation with balanced number of images in every category, and it functions in more complex environments with enlarged and enhanced dataset. In contrast to the lately developed state-ofart methods, the results show that our proposed method achieves higher measurements in terms of accuracy, precision, recall, and F 1-Measure.

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“…In addition, the precision is 99.11% whereas the recall and F1-score are 99.49% and 99.29%, respectively. Because of efficient parameter selection during transfer learning, relying on new data augmentation methods, and using fixed number of images in each category, the proposed model [8,9] use a small number images, reducing variability in the dataset. As observed in Table 4, over the last year years, DL techniques have shown a remarkable improvement for plant disease detection as compared to traditional approaches [8,9] such as SIFT, HoG, and SURF because such methods lack the ability of transfer learning, which is used by DL models.…”

Section: B Implementation Details and Parametersmentioning

confidence: 99%

An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

Alruwaili¹,

El-Ghany²,

Shehab³

2019

IJEAT

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Plant disease detection is used to detect and identify symptoms of plant diseases. Detection of plant diseases through the naked eye is ineffective, especially because there are numerous diseases. Therefore, there is a need to develop low-cost methods to improve rapidity and accuracy of plant disease diagnosis. This paper presents an effective model for plant disease detection by using our developed deep learning approach. Extensive experiments were performed on the PlantVillage dataset, which contains 54,306 images categorized between 38 different classes containing 14 crop species and 26 diseases. Our proposed model demonstrated significant performance improvement in terms of accuracy, recall, precision, and F1-score compared with the existing model used for plant disease detection.

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Background suppressing Gabor energy filtering

Cited by 10 publications

References 28 publications

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

An Efficient Deep Learning Model for Olive Diseases Detection

An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

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