Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Defenses Against Pathogens and Herbivores02:26

Defenses Against Pathogens and Herbivores

26.5K
Plants present a rich source of nutrients for many organisms, making it a target for herbivores and infectious agents. Plants, though lacking a proper immune system, have developed an array of constitutive and inducible defenses to fend off these attacks.
26.5K
Enhanced Elimination of Poison01:26

Enhanced Elimination of Poison

581
Poison can be effectively removed from the gastrointestinal (GI) tract through various decontamination procedures.
Antidotes serve a crucial role in counteracting the effects of poison by inhibiting enzymes responsible for producing harmful drug metabolites. In some cases, these toxic metabolites can be neutralized by endogenous cosubstrates, which are maintained at specific concentrations to prevent interaction with cellular macromolecules and subsequent cell death.
Renal excretion is the...
581
Anticholinesterase Agents: Poisoning and Treatment01:26

Anticholinesterase Agents: Poisoning and Treatment

1.0K
Anticholinesterases, also known as cholinesterase inhibitors, work by blocking the breakdown of acetylcholine, leading to its accumulation in the synaptic cleft. This accumulation indirectly enhances both muscarinic and nicotinic actions. These agents are classified as reversible or irreversible based on their mechanism of action.     
Irreversible agents form a strong bond with the cholinesterase enzyme, making it inactive. The breakdown of the phosphorylated enzyme is...
1.0K
Survival Tree01:19

Survival Tree

160
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
160
Prevention of Further Absorption of Poison01:14

Prevention of Further Absorption of Poison

927
In cases of acute poisoning, the primary objective is to prevent further absorption of the toxic substance into the body. Immediate interventions using various decontamination techniques targeting the gastrointestinal (GI) tract can achieve this. Decontamination is crucial to prevent poison from entering the systemic circulation, which involves washing affected areas with water and mild soap and removing contaminated clothing. Once external decontamination is done, attention must be turned to...
927
Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prompt-Driven Multimodal Segmentation with Dynamic Fusion for Adaptive and Robust Medical Imaging with Applications to Cancer Diagnosis.

Cancers·2025
Same author

Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.

Bioengineering (Basel, Switzerland)·2025
Same author

A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence.

Bioengineering (Basel, Switzerland)·2025
Same author

Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation.

Diagnostics (Basel, Switzerland)·2023
Same author

Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3.

Sensors (Basel, Switzerland)·2023
Same author

Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs.

Biomimetics (Basel, Switzerland)·2023
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Poisonous Plant Prediction Using Explainable Deep Inherent Learning Model.
  1. Home
  3. Poisonous Plant Prediction Using Explainable Deep Inherent Learning Model.

Related Experiment Video

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

Poisonous Plant Prediction Using Explainable Deep Inherent Learning Model.

Ahmed S Maklad1,2, Ashraf Alyanbaawi1, Mohammed Farsi1

  • 1College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia.

Sensors (Basel, Switzerland)
|July 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an Explainable Deep Inherent Learning approach for accurate plant species classification and poisonous status prediction. The method enhances trust in AI for identifying harmful plants, reducing poisoning incidents.

Keywords:
XAIdeep inherent learningexplanation techniquesimage classificationinterpretability

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.7K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

535

Related Experiment Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.7K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

535

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Botany

Background:

  • Global plant species discovery presents challenges in distinguishing beneficial from poisonous varieties.
  • Existing computer vision methods for plant classification and toxicity prediction are limited by data availability.
  • Lack of comprehensive datasets hinders accurate identification and prediction of poisonous plants.

Purpose of the Study:

  • To propose an Explainable Deep Inherent Learning approach for plant species classification and poisonous status prediction.
  • To enhance the accuracy and interpretability of AI models in identifying poisonous plants.
  • To build trust in AI-driven systems for plant identification and safety.

Main Methods:

  • Leveraged advanced computer vision techniques within a Deep Inherent Learning framework.
  • Employed Explainable AI (XAI) for local and global decision-making process clarification.
  • Validated the approach using a dataset of 2500 images from 50 Arabian Peninsula plant species with metadata.

    • Main Results:

    • The XAI model achieved high performance metrics: 0.94 accuracy, 0.96 Precision, 0.96 Recall, and 0.97 F1-Score.
    • Demonstrated effective plant species classification and poisonous status prediction.
    • Provided visual information to enhance user trust in the AI model's predictions.

    Conclusions:

    • The proposed Explainable Deep Inherent Learning approach is effective for plant classification and toxicity prediction.
    • Enhanced interpretability through XAI fosters greater trust in AI for identifying harmful plants.
    • This research contributes to reducing poisoning incidents from harmful plants, benefiting public health.