Paddy Disease Detection
Paddy Disease Detection - Related Videos
06:02
Detection of Neuritic Plaques in Alzheimer's Disease Mouse Model
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Cited by 66 •
2011
One of the pathological characteristics of AD is the formation of Amyloid β protein positive neuritic plaques. In this protocol we describe two methods to detect neuritic plaques in transgenic AD model mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thioflavin S staining method.
04:37
Detecting Neurodegenerative Disease-Associated Protein α-Synuclein Using ELISA
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2025
Source: Bétemps, D., et al., Detection of Disease-associated α-synuclein by Enhanced ELISA in the Brain of Transgenic Mice Overexpressing Human A53T Mutated α-synuclein. J. Vis. Exp. (2015).This video demonstrates the procedure to detect neurodegenerative disease-associated protein α-synuclein in brain tissue from transgenic mice using ELISA. The steps include tissue homogenization, centrifugation, and ELISA detection of α-synuclein protein to quantify its disease-associated forms.
01:23
Immunohistochemical Detection of Lewy Bodies in a Diseased Human Brain Tissue Section
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2025
Incubate with hydrogen peroxide solution to inhibit endogenous peroxidases and prevent non-specific staining.
12:01
Detection of Disease-associated α-synuclein by Enhanced ELISA in the Brain of Transgenic Mice Overexpressing Human A53T Mutated α-synuclein
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Cited by 5 •
2015
An ELISA offering a novel quantitative approach is described. It specifically detects disease-associated α-synuclein (αSD) in a transgenic mouse model (M83) of synucleinopathy using several antibodies against either the Ser129 phosphorylated αS form or the C-terminal part of the protein.
08:32
Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
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Cited by 53 •
2015
This study successfully adapted human videofluoroscopic swallowing study (VFSS) methods for use with murine disease models for the purpose of facilitating translational dysphagia research.
FAQs
Image preprocessing reduces noise in leaf images before visual analysis begins. By producing cleaner input, it helps subsequent feature extraction focus on disease-relevant patterns rather than unwanted image variation. This step supports more consistent use of color, texture, and lesion information, strengthening the engineering workflow that leads to machine-learning or computer-vision classification.
These visual features provide different evidence about a plant’s condition. Color can contribute visible distinctions, texture can describe surface appearance, and lesion patterns can indicate localized changes on leaves. Combining them gives a model more information than relying on one visual characteristic alone, which can support classification of disease conditions from captured plant images.
Machine-learning and computer-vision models use extracted visual information to classify disease conditions. Their role follows image preparation and feature extraction, creating a staged process rather than treating the raw image as the final result. In engineering applications, this classification capability can help transform visual observations into a more systematic form of crop-health monitoring.
Manual inspection depends on people examining plants directly, whereas an engineered system can analyze images or recorded field observations through repeatable computational steps. Automated analysis may reduce reliance on manual inspection and make surveillance more scalable across farms. It does not remove the importance of field observations, which remain another possible source of disease-related information.
A practical system may combine cameras or other image-capture tools with mobile devices, automated monitoring platforms, and software for image analysis. These components collect or process observations from leaves and fields, allowing the workflow to extend beyond a single inspection. Their integration can support broader surveillance and generate data for precision-agriculture decision-support systems.
The approach is useful when growers or monitoring systems need early information about crop-health conditions across farms. Earlier identification can support targeted crop management, while scalable surveillance can organize observations for precision agriculture. From an engineering perspective, the collected information also contributes to future decision-support systems that connect sensing, classification, and farm-management activities.