Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model

• Author(s): Tata Naga Nitin ; Ankur Yadav ; Dr. A. Anbarasi
• Paper ID: 1707799
• Page: 1021-1026
• Published Date: 28-04-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 10 April-2025

Abstract

the agricultural sector is a key driver of a nation's economic growth, especially in India, where it serves as a primary source of livelihood for millions in rural areas. One of the major challenges facing agriculture is plant diseases, which can be triggered by a variety of factors such as synthetic fertilizers, outdated farming practices, and environmental conditions. These diseases can severely impact crop yield, ultimately affecting the economy. To tackle this issue, researchers have increasingly turned to AI and Machine Learning techniques for plant disease detection. This research survey provides an in-depth review of common plant leaf diseases, evaluates both traditional and deep learning approaches for disease identification, and highlights available datasets. Additionally, it investigates the role of Explainable AI (XAI) in improving the transparency of deep learning models, making their decisions more interpretable for end-users. By synthesizing this knowledge, the survey offers valuable insights for researchers, practitioners, and stakeholders, driving the development of effective and transparent solutions for managing plant diseases and promoting sustainable agriculture.

Citations

IRE Journals:
Tata Naga Nitin , Ankur Yadav , Dr. A. Anbarasi "Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 1021-1026

IEEE:
Tata Naga Nitin , Ankur Yadav , Dr. A. Anbarasi "Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model" Iconic Research And Engineering Journals, 8(10)