DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

Ilyas, Talha; Umraiz, Muhammad; Khan, Abbas; Kim, Hyongsuk

doi:10.3389/fpls.2021.591333

Cited by 21 publications

(22 citation statements)

References 76 publications

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“…(1) The first is an input layer, in which the five-dimensional vector for each residue of amino acid is passed to the preceding layer for features extraction. (2) The second layer is a convolution layer, which extracts the low- to high-level features by processing the grid pattern data [ 73 ]. A convolution layer performs a specialized type of linear operation, and the data, which are stored in an array of numbers and small grid parameters called the kernel for optimizable feature extraction, are applied at every position of the input matrix.…”

Section: Materials and Methodsmentioning

confidence: 99%

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Siraj

Lim

Tayara

et al. 2021

Genes

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Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

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Section: Materials and Methodsmentioning

confidence: 99%

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Siraj

Lim

Tayara

et al. 2021

Genes

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“…Wang et al [ 55 ] developed a system based on Mask R-CNN to segment diseases in tomatoes. Similarly, in the field of agriculture, Khan et al [ 56 ] proposed a cascaded encoder–decoder (CED-Net) architecture for detecting precise locations of weeds and crops on farmland [ 57 ].…”

Section: Related Workmentioning

confidence: 99%

An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN

Afzaal

Bhattarai

Pandeya

et al. 2021

Sensors

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Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.

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“…In the current decade, deep learning has revolutionized the artificial intelligence (AI) realm and continues to do so. Deep learning algorithms have shown remarkable performance practically, especially in computer vision [3][4][5][6]. In this context, machine vision approaches are a hot research area where robotic solutions are developed to automate the processes [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques are also not robust as they were not aimed for the real time applications in the field. Furthermore, the existing literature [4] on strawberries does not discuss the gray mold disease problem which spreads heavily from the overgrown strawberries, which motivated us to investigate this problem. Since the proposed study is for the real-field scenarios, therefore we consider the following few challenges.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Context Aggregation for Strawberry Fruit Recognition and Disease Phenotyping

Ilyas¹,

Khan

Umraiz

et al. 2021

IEEE Access

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Timely harvesting and disease identification of strawberry fruits is a major concern for commercial level cultivators. Failing to harvest the grown strawberries can result in the fruit rotting which makes their damaged tissues more prone to grey mold pathogens. Immediate removal of the overgrown or diseased strawberries is inevitable to curb the mass spreading of the pathogen. In this paper, we propose a deep learning-based framework to identify three different strawberry fruit classes (unripe, partially ripe and ripe), as well as a class of overgrown or diseased strawberries. We equip the proposed convolutional encoderdecoder network with three different modules. One for adaptively controlling receptive filed size of the network to detect objects of multiple sizes. Second for controlling the flow of salient features (information) to the deeper layers of the network and the other for controlling the architecture's computational complexity. These modules combined, outperform the previous state-of-the-art semantic segmentation networks on the task of strawberry fruit phenotyping. We also introduce a dataset collected from different farms to evaluate the performance of the network. Quantitative and qualitative results show that notwithstanding heterogeneity in the data and the effect of the real-field variations, our approach produced remarkable results with a 3% increase in mean intersection over union as compared to the other state-of-the-art networks and was able to recognize diseased fruits with a precision of 92.45%.

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DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

Cited by 21 publications

References 76 publications

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN

Multi-Scale Context Aggregation for Strawberry Fruit Recognition and Disease Phenotyping

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