mlsanityData to 7 Oct 2026

Leaf-Based Plant Disease Detection and Explainable AI

arXiv 2404.16833 ↗First posted 17 Dec 2023cs.CV cs.AI cs.LGcited by 1

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Abstract (arXiv)

The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synthetic fertilizers, archaic practices, environmental conditions, etc., which impact the farm yield and subsequently hinder the economy. To address this issue, researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users. By consolidating this knowledge, the survey offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 30 references that are in the map. First posted on arXiv 17 Dec 2023.

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5 of 30 references.

References · 5

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 216
  3. 2020LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosiscited by 18
  4. 2021Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Techniquecited by 2
  5. 2022T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Networkcited by 2

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