Authors: Saurav Sagar, Mohammed Javed, David S Doermann

Published on: December 17, 2023

Impact Score: 8.2

Arxiv code: Arxiv:2404.16833

Summary

• What is new: The use of Explainable AI (XAI) to make deep learning models’ decisions in plant disease detection more interpretable to end-users.
• Why this is important: Plant diseases significantly impact agricultural yield and economy, with detection being hampered by various factors including the use of synthetic fertilizers, outdated farming practices, and environmental conditions.
• What the research proposes: The research surveys AI and Machine Learning techniques, particularly focusing on traditional and deep learning methods, to detect plant leaf diseases and employs Explainable AI to improve the clarity of these detections for users.
• Results: A comprehensive overview of plant leaf diseases, an evaluation of AI techniques for disease detection, and a summary of available datasets to aid in developing effective disease detection models.

Technical Details

Technological frameworks used: Deep Learning, Explainable AI

Models used: Traditional and deep learning models for plant disease detection

Data used: Datasets containing images and information on common plant leaf diseases

Potential Impact

Agricultural sector, companies producing synthetic fertilizers, agricultural technology firms, and startups focusing on AI in farming

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