Fast and Accurate Detection and Classification of Plant Diseases
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TLDR
The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases and can achieve 20% speedup over the approach proposed in [1].Abstract:
We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].read more
Citations
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Journal ArticleDOI
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
TL;DR: A new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks, which is able to recognize 13 different types of plant diseases out of healthy leaves.
Journal ArticleDOI
Deep Learning for Tomato Diseases: Classification and Symptoms Visualization
TL;DR: A large dataset compared to the state-of-the art is used and the proposed deep model performs dramatically shallow models, and they can be used as a practical tool for farmers to protect tomato against disease.
Journal ArticleDOI
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.
Guan Wang,Yu Sun,Jianxin Wang +2 more
TL;DR: The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set.
Proceedings ArticleDOI
Plant Disease Detection Using Image Processing
Sachin D. Khirade,A.B. Patil +1 more
TL;DR: The methods used for the detection of plant diseases using their leaves images are discussed and some segmentation and feature extraction algorithm used in the plant disease detection are discussed.
Journal ArticleDOI
Using deep transfer learning for image-based plant disease identification
TL;DR: This work study transfer learning of the deep convolutional neural networks for the identification of plant leaf diseases and consider using the pre-trained model learned from the typical massive datasets, and then transfer to the specific task trained by the authors' own data.
References
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Survey over image thresholding techniques and quantitative performance evaluation
Mehmet Sezgin,Bulent Sankur +1 more
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Journal ArticleDOI
Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance
Till Rumpf,Anne-Kathrin Mahlein,Ulrike Steiner,Erich-Christian Oerke,H. W. Dehne,Lutz Plümer +5 more
TL;DR: A procedure for the early detection and differentiation of sugar beet diseases based on Support Vector Machines and spectral vegetation indices to discriminate diseased from non-diseased sugar beet leaves and to identify diseases even before specific symptoms became visible.