Proceedings ArticleDOI
A review on: Various techniques of plant leaf disease detection
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TLDR
Various techniques of plant disease detection is reviewed and discussed in terms of various parameters in this paper.Abstract:
An expected 70% of Indian economy relies upon agribusiness. Since there is developing Indian population, which is increasingly dependent on the agricultural yield, generation of the harvests must be improved. The end goal is kept in mind to develop progressively the diseases need to be examined in earlier. Diseases are investigated utilizing different image processing techniques the image processing is the technique which process the digital information stored in the form of images. The plant disease detection is the technique which detects disease from the input images. The plant disease detection consists of three steps: initially the image that is fed to input terminal is preprocessed, thereafter features of the image is analyzed according to their features segmentation is applied and in the last step image is classified using any of the classifier. In this paper, various techniques of plant disease detection is reviewed and discussed in terms of various parameters.read more
Citations
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Proceedings ArticleDOI
Plant Disease Detection Techniques: A Review
Gurleen Kaur Sandhu,Rajbir Kaur +1 more
TL;DR: The potential of the methods of plant leaves disease detection system that facilitates the advancement in agriculture is reviewed, which includes various phases such as the image acquisition, image segmentation, feature extraction and classification.
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Estimation of Fusarium Head Blight Severity Based on Transfer Learning
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Book ChapterDOI
Hybridizing Convolution Neural Networks to Improve the Accuracy of Plant Leaf Disease Classification
Bhavana Nerkar,Sanjay N. Talbar +1 more
TL;DR: A hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and speed of leaf classification and it was observed that the delay is reduced, while the accuracy is improved by the most effective classifiers.
Proceedings ArticleDOI
Comparison Analysis of the Artificial Neural Network Algorithm and K-Means Clustering in Gorontalo Herbal Plant Image Identification System
TL;DR: The objective of this study was to analyze the comparison between artificial neural network algorithm and k-means clustering to see the extent of the effectiveness of this algorithm on the identification of Gorontalo herbal plant image.
References
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BookDOI
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Journal Article
Learning with kernels : Support vector machines, regularization, optimization, and beyond
Journal ArticleDOI
Support vector machine classification and validation of cancer tissue samples using microarray expression data
Terrence S. Furey,Nello Cristianini,Nigel Duffy,David W. Bednarski,Michèl Schummer,David Haussler +5 more
TL;DR: A new method to analyse tissue samples using support vector machines for mis-labeled or questionable tissue results and shows that other machine learning methods also perform comparably to the SVM on many of those datasets.
Journal ArticleDOI
Detection of plant leaf diseases using image segmentation and soft computing techniques
Vijai Singh,Anoop Misra +1 more
TL;DR: An algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases and also covers survey on different diseases classification techniques that can be used for plant leaf disease detection.
Journal ArticleDOI
Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging
TL;DR: This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes the current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted.
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