SLIC_SVM based leaf diseases saliency map extraction of tea plant

Sun, Yunyun; Jiang, Zhaohui; Zhang, Liping; Dong, Wei; Rao, Yuan

doi:10.1016/j.compag.2018.12.042

Cited by 92 publications

(42 citation statements)

References 18 publications

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“…For training and testing proposed artificial neural network model, seven selected coefficients of shape and 16 color characteristics were extracted from each pest image as inputs. Sun et al [22] have combined SLIC (simple linear iterative cluster) with SVM (support vector machine) classifier to detect diseases on tea plant. Their algorithm improved the prediction accuracy of disease images taken with complex backgrounds but needed more pre-treatments to reduce interference.…”

Section: Related Workmentioning

confidence: 99%

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Xing

Lee

2019

Sensors

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Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.

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Section: Related Workmentioning

confidence: 99%

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Xing

Lee

2019

Sensors

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“…Fungal pathogens that affect tea plant leaves can lead to a significant reduction in their quantity and quality, resulting in a loss of revenue ( Gulati et al., 1993 ; Baby, 2002 ; Wang L. et al., 2016 ; Wang Y. et al., 2016 ; Cheng et al., 2019 ; Sun et al., 2019 ). Approximately 507 fungal pathogenic species are associated with tea plants ( Chen and Chen, 1990 ), and among them Exobasidium vexans mainly attacks tea leaves which make ~40% yield loss ( Sun et al., 2019 ; Chaliha and Kalita, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Climate-Fungal Pathogen Modeling Predicts Loss of Up to One-Third of Tea Growing Areas

Tibpromma

Dong

Ranjitkar

et al. 2021

Front. Cell. Infect. Microbiol.

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Climate change will affect numerous crops in the future; however, perennial crops, such as tea, are particularly vulnerable. Climate change will also strongly influence fungal pathogens. Here, we predict how future climatic conditions will impact tea and its associated pathogens. We collected data on the three most important fungal pathogens of tea (Colletotrichum acutatum, Co. camelliae, and Exobasidium vexans) and then modeled distributions of tea and these fungal pathogens using current and projected climates. The models show that baseline tea-growing areas will become unsuitable for Camellia sinensis var. sinensis (15 to 32% loss) and C. sinensis var. assamica (32 to 34% loss) by 2050. Although new areas will become more suitable for tea cultivation, existing and potentially new fungal pathogens will present challenges in these areas, and they are already under other land-use regimes. In addition, future climatic scenarios suitable range of fungal species and tea suitable cultivation (respectively in CSS and CSA) growing areas are Co. acutatum (44.30%; 31.05%), Co. camelliae (13.10%; 10.70%), and E. vexans (10.20%; 11.90%). Protecting global tea cultivation requires innovative approaches that consider fungal genomics as part and parcel of plant pathology.

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“…Alternatively, color imaging is inexpensive and is easy to handle. Color imaging can capture the color and texture information of an object, and has been implemented to detect and assess plant diseases [18][19][20]. For Fusarium or FHB detection, researchers have explored the usefulness of color imaging.…”

Section: Introductionmentioning

confidence: 99%

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

et al. 2019

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Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.

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SLIC_SVM based leaf diseases saliency map extraction of tea plant

Cited by 92 publications

References 18 publications

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Climate-Fungal Pathogen Modeling Predicts Loss of Up to One-Third of Tea Growing Areas

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

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