Poisonous Plant Prediction Using Explainable Deep Inherent Learning Model

TL;DR

This paper introduces an explainable AI model to classify and predict poisonous plants using images and metadata, aiming to prevent poisoning incidents.

Contribution

The novel contribution is an Explainable Deep Inherent Learning model that provides interpretable predictions for plant toxicity.

Findings

The XAI model achieved high accuracy (0.94) and F1-score (0.97) in predicting plant toxicity.

The model was validated using 2500 images of 50 plant species from the Arabian Peninsula with metadata.

Explainable AI techniques enhanced trust in the model's decision-making process.

Abstract

The increasing global discovery of plant species presents both opportunities and challenges, particularly in distinguishing between beneficial and poisonous varieties. While computer vision techniques show promise for classifying plant species and predicting toxicity, the lack of comprehensive datasets including images, scientific names, descriptions, local names, and poisonous status complicates these predictions. In this paper, we propose an Explainable Deep Inherent Learning approach that leverages advanced computer vision techniques for effective plant species classification and poisonous status prediction. The proposed Deep Inherent Learning method was validated using different explanation techniques, and Explainable AI (XAI) was employed to clarify decision-making processes at both the local and global levels. Additionally, we provide visual information to enhance trust in the proposed method. To validate the efficacy of our approach, we present a case study involving 2500 images of 50 different plant species from the Arabian Peninsula, enriched with essential metadata. This research aims to reduce the incidence of poisoning from harmful plants, thereby benefiting individuals and society. Our experimental results demonstrate strong performance, with the XAI model achieving accuracy, Precision, Recall, and F1-Score of 0.94, 0.96, 0.96 and 0.97, respectively. By enhancing interpretability, our study fosters greater trust in AI-driven plant classification systems.

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