Building a Globally Optimized Computational Intelligent Image Processing Algorithm for On-Site Inference of Nitrogen in Plants

Sulistyo, Susanto Budi; Woo, Wai Lok; Dlay, S.S.; Gao, Bin

doi:10.1109/mis.2018.111144506

Cited by 24 publications

(14 citation statements)

References 6 publications

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“…The rapid development of intelligent agriculture and precision agriculture in recent years has led to the widespread use of computer image processing technologies to solve diverse problems within agricultural sciences. For example, these technologies have been used to estimate plant nutrient content [2][3][4], classify plant species [5], and identify plant diseases [6,7]. In particular, deep neural network and genetic algorithms have been used in combination to estimate nitrogen content in wheat leaves [2][3][4], which represents a considerable improvement over other existing methods.…”

Section: Introductionmentioning

confidence: 99%

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

2019

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The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.

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Section: Introductionmentioning

confidence: 99%

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

2019

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“…Different from approaches using hand-crafted features, deep learning networks use hierarchical structures to automatically extract features. Due to the breakthroughs made by deep learning in an increasing number of image-processing tasks [ 25 , 26 , 27 , 28 ], some research has started to apply deep learning approaches for melanoma recognition. Codella et al proposed a hybrid approach, integrating convolutional neural network (CNN), sparse coding and support vector machines (SVMs) to detect melanoma [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Shen

2018

Sensors

493

260

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Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

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“…For this reason, Refs. [23][24][25] presented a method for predicting nitrogen content in wheat plant. Their methods were based on a segmentation algorithm that was trained in three light intensities for separating wheat plants from the background.…”

Section: Introductionmentioning

confidence: 99%

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

et al. 2020

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The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

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Building a Globally Optimized Computational Intelligent Image Processing Algorithm for On-Site Inference of Nitrogen in Plants

Cited by 24 publications

References 6 publications

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

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