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

Revathi, P.; Hemalatha, M.

doi:10.5120/7160-8271

Cited by 12 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their results gives a precision ranging from 83% to 94%. P. Revathi et [11] al., have used homogeneous segmentation Edge detection techniques. They have used almost spotted eight cotton leave diseases using neural network.…”

Section: Literature Surveymentioning

confidence: 99%

Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

Gnanasaravanan¹,

Tharani²,

Sahu

2021

ijcci

View full text Add to dashboard Cite

Farming profitability is something on which economy profoundly depends. This is the one reason that sickness recognition in plants assumes a critical job in farming field, as having infection in plants are very common. In the event that legitimate consideration isn't taken here, it causes genuine consequences for plants and because of which particular item quality, amount or profitability is influenced. This paper displays an algorithm for image segmentation technique which is utilized for automatic identification and classification plant leaf infections. It additionally covers review on various classification techniques that can be utilized for plant leaf ailment discovery. As the infected regions vary in length it is difficult to develop a feature vector of identical finite length representing all the sequences. A simple method to go around this issue is given by Recurrent Neural Networks (RNN). In this work we separate a feature vector through the use of Long Short-Term Memory (LSTM) recurrent neural networks. The LSTM network recursively repeats and concentrates two limited vectors whose link yields finite length vector portrayal.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

Gnanasaravanan¹,

Tharani²,

Sahu

2021

ijcci

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…How to segment the diseased leaves of crops with high efficiency and high quality is a research hotspot. In the last two decades, traditional image processing techniques, such as edge detection, color space transformation, feature space transformation, etc., were used to achieve the extraction and recognition of lesions [5,6]. Using the grayscale structure intensity histogram of channel H (from the HSV color space) and channel a (from the L*a*b* color space), one can find the pixel value that can best separate healthy and diseased tissues, and segment the lesions [7].…”

Section: Introductionmentioning

confidence: 99%

A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Huang

et al. 2021

Agriculture

View full text Add to dashboard Cite

Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions.

show abstract

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

Cited by 12 publications

References 9 publications

Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

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

A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Contact Info

Product

Resources

About