Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases

Tian, Yuan; Zhao, Chunjiang; Lu, Shenglian; Guo, Xinyu

doi:10.1080/10798587.2011.10643166

Cited by 78 publications

(19 citation statements)

References 17 publications

(25 reference statements)

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“…The system proposed in [5] presents a SVM-based Multiple Classifier System (MCS) for pattern recognition of wheat leaf diseases. Based on the infected images of various rice plants the paper [6] describes a software prototype system for rice disease detection.…”

Section: Existing Workmentioning

confidence: 99%

Tea Leaf Diseases Recognition using Neural Network Ensemble

Karmokar¹,

Ullah²,

Siddiquee³

et al. 2015

IJCA

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This paper proposes a tea leaf diseases recognizer (TLDR), an initiative to recognize diseases of the tea leaf. In TLDR, at first the image of the tea leaf is cropped, resized and converted to its threshold value in the image processing. Then feature extraction method is applied. Neural Network Ensemble (NNE) was used for pattern recognition. The extracted features are passed to the ANN along with the disease type and the ANN is trained. When a new image is uploaded into the system the most suitable match is found and the disease is returned. After going through the testing process 91 % of accuracy was found. The proposed solution would support the tea industry of Bangladesh to grow in the global market and also increase its tea production by minimizing the effect of tea leaf diseases.

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Section: Existing Workmentioning

confidence: 99%

Tea Leaf Diseases Recognition using Neural Network Ensemble

Karmokar¹,

Ullah²,

Siddiquee³

et al. 2015

IJCA

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show abstract

“…They reported that the max-wins-voting SVM with Gaussian radial Basis kernel recorded the best classification accuracy with 88.2%. Tian et al [64] developed a multiple classifier system (MCS) based on the support vector machine for pattern recognition of wheat leaf diseases. Three different features, namely, color, texture and shape, are used as training sets.…”

Section: Fruit Disease Recognition and Classificationmentioning

confidence: 99%

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Dubey

Jalal

2015

Journal of Intelligent Systems

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Images are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed imageprocessing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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“…Tian et.al. [10] suggested a new method of Multi-Classifier System based on SVM for pattern recognition of wheat leaf diseases with high recognition accuracy. Diseased leaf samples with Powdery Mildew, Rust Puccinia Triticina, Leaf Blight, and Puccinia Striiformis were collected in the field and images were captured before a uniform black background.…”

Section: Related Workmentioning

confidence: 99%

Yellow Rust Extraction in Wheat Crop based on Color Segmentation Techniques

Khatra¹

2013

IOSRJEN

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The presented work presents a color based segmentation techniques for extraction of yellow rust in whet crop images. Accurate segmentation of yellow rust in wheat crop images is very part of assessment of disease penetration into the wheat crop. And in turn to o take the necessary preventive action for minimizing the crop damage. The jpeg images acquired from CCD camera are read into the matlab tool and a color based segmentation algorithm is performed to segment the yellow rust. The segmentation of color is performed base on k-means algorithm.

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Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases

Cited by 78 publications

References 17 publications

Tea Leaf Diseases Recognition using Neural Network Ensemble

Tea Leaf Diseases Recognition using Neural Network Ensemble

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Yellow Rust Extraction in Wheat Crop based on Color Segmentation Techniques

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