Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve, Bruce; AlSuwaidi, Ali

doi:10.1109/jstars.2017.2788426

Cited by 44 publications

(18 citation statements)

References 82 publications

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“…Alsuwaidi et al [34] used a ground-breaking analytical classification system in which they incorporated adaptive feature collection, novelty identification and ensemble learning with the hyper spectral datasets. Singh et al [35] presented an automated approach to differentiate between Neem and Bakain using the texture characteristics of its leaves and then they used tree classifier to separate them in separate classes.…”

Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Yadav¹,

Akanksha²,

Yadav³

2020

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Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

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Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Yadav¹,

Akanksha²,

Yadav³

2020

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“…In novelty detection, the effectiveness of using multiple detectors has also been demonstrated. (22) In this article, we propose an ensemble novelty detection method for the accurate detection of unknown storing positions. We also demonstrate a method of adjusting an important parameter that maximizes the effect of ensemble novelty detection while eliminating the burden of end users.…”

Section: Ensemble Novelty Detectionmentioning

confidence: 99%

Unknown On-Body Device Position Detection Based on Ensemble Novelty Detection

Saito¹,

Fujinami²

2020

Sensors and Materials

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In recent years, on-body device position recognition has attracted a lot of attention from the ubiquitous computing community with a view to providing reliable services to users. The existing work has focused on the recognition of classes included in a training dataset, but handling a new position that the recognition system does not know is still impossible. The unknown position should be handled in an appropriate way to avoid incorrect behavior and adapt to each user's way of carrying the device. In this article, we propose a new detection method based on the ensemble learning principle, in which the final results are obtained from a collection of judgments by a weak novelty detector. We devise a method of finding a threshold that maximizes overall accuracy, rather than a mere majority vote. This method is evaluated with three datasets and various conditions to confirm the effectiveness of ensemble novelty detection and the threshold estimation method.

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“…Two HSI datasets were considered in our experiment: UoM and Bonn datasets, captured in controlled environment (dark room or chamber) by the UoM and Bonn systems, respectively [46,47]. A powerful and constant light source was used in the UoM case, while six lamps were surrounding the Bonn system to lighten the sample plate.…”

Section: Datasetsmentioning

confidence: 99%

Combining spectral and texture features in hyperspectral image analysis for plant monitoring

AlSuwaidi

Grieve

Yin

2018

Meas. Sci. Technol.

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A texture enhanced spectral analysis framework is proposed for classifying hyperspectral images of plants of different conditions. Differentiating different plant conditions is important to precision agriculture as it helps detect diseases and stresses and optimise growth control. Advanced machine learning techniques are used to identify distinctive features in the spectral domain of hyperspectral images. In addition, texture properties are explored in the subband images. The framework integrates these two levels of properties at both feature extraction and classifying decision stages. The main crux of the work lies in the use of the significant spectral and texture features and a decision fusion mechanism to enhance the image properties, thus improving classification accuracy. Two hyperspectral datasets, originated from proximal hyperspectral systems, were used in the evaluation and significant improvements in classification accuracy achieved.

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Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Cited by 44 publications

References 82 publications

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Unknown On-Body Device Position Detection Based on Ensemble Novelty Detection

Combining spectral and texture features in hyperspectral image analysis for plant monitoring

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