Crop Disease Leaf Image Segmentation Method Based on Color Features

Wang, Lidi; Yang, Tao; Tian, Yuan

doi:10.1007/978-0-387-77251-6_78

Cited by 11 publications

(6 citation statements)

References 2 publications

(2 reference statements)

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“…Approaches include active polygons [13], [14] and active contours [15], [16]. A number of signatures from simple background models to shape priors and color signatures [17], [18] have been used for detection. In [19] color histograms are used to separate leaves from the background, and partially overlapping leaves are split using boundary shape cues.…”

Section: Related Workmentioning

confidence: 99%

A Pyramid CNN for Dense-Leaves Segmentation

Morris

2018

2018 15th Conference on Computer and Robot Vision (CRV)

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Automatic detection and segmentation of overlapping leaves in dense foliage can be a difficult task, particularly for leaves with strong textures and high occlusions.We present Dense-Leaves, an image dataset with ground truth segmentation labels that can be used to train and quantify algorithms for leaf segmentation in the wild. We also propose a pyramid convolutional neural network with multiscale predictions that detects and discriminates leaf boundaries from interior textures. Using these detected boundaries, closedcontour boundaries around individual leaves are estimated with a watershed-based algorithm. The result is an instance segmenter for dense leaves. Promising segmentation results for leaves in dense foliage are obtained.

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Section: Related Workmentioning

confidence: 99%

A Pyramid CNN for Dense-Leaves Segmentation

Morris

2018

2018 15th Conference on Computer and Robot Vision (CRV)

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“…[7] a kind of color feature is applied to obtain the feature map, which combines RGB model and HSI model. [8] we determine the RGB threshold value, through counting the characteristic parameter of stochastic picture element in the different image spot. [9] Segmentation is the most important part to detect paddy disease early and accurately, we presented an application of image processing technique and a new method Gaussian Mean (GM) for segmenting paddy disease.…”

Section: Literature Reviewand Related Studiesmentioning

confidence: 99%

Homogenous Segmentation based Edge Detection Techniques for Proficient Identification of the Cotton Leaf Spot Diseases

Revathi¹,

Hemalatha²

2012

IJCA

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In this work we express technological strategies using mobile captured symptoms of cotton leaf spot images and classify the diseases using neural network. The system has been trained to achieve intelligent farming for rural area farmers, including early recognition of diseases in grows, selective fungicide application,etc..This research work proposes an automatic image preprocessing techniques. At first, the captured images are processed for improvement. Other edge detectors presented in earlier works can detect edges on different size objects. In this Research work, a homogeneity operator can take the difference of the center pixel and a pixel that is two or three pixels away. The major objective of this Research work is to use Homogeneity-based edge detector segmentation, which takes the result of any edge detector and divides it by the average value of the area. This work has been implemented in the real time software and produces best results.

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“…In the early studies of plant disease recognition, feature descriptors were designed to extract internal features from pictures obtained in the laboratory or the agricultural production environment using edge detection, color space transform, feature space transform theories, etc. [3][4][5]. These features were usually classified using a support vector machine (SVM) and linear discrimination, among others.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation

et al. 2021

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Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.

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Crop Disease Leaf Image Segmentation Method Based on Color Features

Cited by 11 publications

References 2 publications

A Pyramid CNN for Dense-Leaves Segmentation

A Pyramid CNN for Dense-Leaves Segmentation

Homogenous Segmentation based Edge Detection Techniques for Proficient Identification of the Cotton Leaf Spot Diseases

An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation

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