Image-Based Plant Recognition by Fusion of Multimodal Information

Liu, Jen-Chang; Chiang, Chieh-Yu; Chen, Sam

doi:10.1109/imis.2016.60

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…The watershed algorithm is also a popular image segmentation method, which is often used for single tree crown delineation and kernel and leaf segmentation [92]. In addition, some of the methods that have been applied effectively to gray-scale, color, and multispectral images have been optimized purposefully and play key roles in hyperspectral image segmentation, such as Grabcut [93], the method of Otsu [94], and edge detection [95,96].…”

Section: Diseases Classification Is the Attempt To Identify And Label The Pathogens Affecting The Plant Simultaneouslymentioning

confidence: 99%

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

et al. 2020

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The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

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Section: Diseases Classification Is the Attempt To Identify And Label The Pathogens Affecting The Plant Simultaneouslymentioning

confidence: 99%

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

et al. 2020

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“…Most existing computational models for multisensory learning apply explicitly weighted connections, and the sensor information is integrated using early (Wei et al, 2010 ) or late (de Boer et al, 2016 ; Liu et al, 2016 ) fusion techniques. The weighted connections are usually tuned in a data-driven manner, whereby the data distribution directly affects the multisensory binding.…”

Section: Related Workmentioning

confidence: 99%

Expectation Learning for Stimulus Prediction Across Modalities Improves Unisensory Classification

Barros¹,

Eppe²,

Parisi³

et al. 2019

Front. Robot. AI

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Expectation learning is a unsupervised learning process which uses multisensory bindings to enhance unisensory perception. For instance, as humans, we learn to associate a barking sound with the visual appearance of a dog, and we continuously finetune this association over time, as we learn, e.g., to associate high-pitched barking with small dogs. In this work, we address the problem of developing a computational model that addresses important properties of expectation learning, in particular focusing on the lack of explicit external supervision other than temporal co-occurrence. To this end, we present a novel hybrid neural model based on audiovisual autoencoders and a recurrent self-organizing network for multisensory bindings that facilitate stimulus reconstructions across different sensory modalities. We refer to this mechanism as stimulus prediction across modalities and demonstrate that the proposed model is capable of learning concept bindings by evaluating it on unisensory classification tasks for audiovisual stimuli using the 43,500 Youtube videos from the animal subset of the AudioSet corpus.

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“…Although most plant identification systems consider only plant organ images and their combination, some authors adopt external information (i.e., information that are not contained in the plant images) to help the recognition process. For example, in [Liu et al 2016] proposes to automatically identify plant species using images from different plant organs, such as flowers and leaves, and also by taking into consideration the location information (represented by GPS coordinates) about the region where the plant specimens have been acquired.…”

Section: A Brief Review On Automatic Plant Identification Systemsmentioning

confidence: 99%

Classificação de Espécies de Plantas Usando Extreme Learning Machine

Britto¹,

Pacífico²

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.

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Image-Based Plant Recognition by Fusion of Multimodal Information

Cited by 11 publications

References 4 publications

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

Expectation Learning for Stimulus Prediction Across Modalities Improves Unisensory Classification

Classificação de Espécies de Plantas Usando Extreme Learning Machine

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