Year: 2025
Type: review
Study design: Systematic Review
Abstract: This paper work utilizes a review analysis method to determine diseases that affect potato crops which include; late blight, early blight and bacterial wilt In addition, it explores the possible management techniques of computer vision system and machine learning algorithms. The review includes only the articles published in the last five years and contains the following keywords as major terms: potato diseases, machine learning, computer vision. To resolve this, this paper reviewed 17 current published articles from academic databases such as Google Scholar, PubMed, and Scopus after applying specific filter criteria. The study identifies deep learning as the main technique in disease detection in fields that point to the pursuit of AI and ML in the agricultural fields. In addition, the research addresses the effectiveness of alarm RCA in industrial safety, natural services to enhance urban water management, and environmental stress factors affecting plant species’ survival. Consequently, the findings suggest that due to increasing industrialization, heavy metal contamination is becoming a increasingly difficult issue; but through the use of plant exudates phytoremediation offers a solution to some of these challenges. The study establishes a link between plant health, ecological changes, and sustainable practices, this calls for improvement in research and use of proper management measures that will counter the said diseases and the effects they have on production.
Source: AI and Machine Learning Advances
Authors: Kingdom Saudi Arabia, Hussein Ahmed
Institution: Karachi Institute of Economics and Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4412455358