Improving Plant Disease Classification with Deep-Learning-Based Predicton Model

Improving Plant Disease Classification with Deep-Learning-Based Predicton Model

Authors- Anzil Mohannad Muthalib, Raashid M, Vishnuprasad P, Vishnu VK

Abstract-– Plants are the major contributors to food provision worldwide. Different environmental conditions cause plant diseases which leads to great production loss. But manual identification of plant diseases is a laborious and prone-to-error activity. It may be an unreliable means of detecting and controlling plant diseases. Embracing cutting-edge technologies like Machine Learning (ML) and Deep Learning (DL) can solve these issues by making it possible to identify plant diseases at an early stage. In this article, the latest trends in the application of ML and DL methods for plant disease identification are discussed. The study the articles published from 2015 to 2022, and the experiments in this research illustrate how these methods work to enhance plant disease detection efficiency and accuracy. This research also discusses challenges and limitations when employing ML and DL for the identification of plant disease, including limitations in data availability, image quality, and distinction between diseased and healthy plants. The study gives significant information for practitioners, researchers of plant disease detection, and industry experts through solutions to the limitations and challenges that they present and through offering a broad vision of where this field of study stands in relation to its present research state, identifying the strengths and weaknesses of these methods, and presenting some possible solutions that can address their challenges of implementation.

DOI: /10.61463/ijset.vol.13.issue2.328