AI-powered Innovations in Agriculture: A Systematic Review on Plant Disease Detection and Classification

One of the primary needs of humans is food, which can be obtained through farming. Not only does agriculture meet the necessities of humankind, but it is also a primary source of employment. Agriculture is the main driver of employment and economic growth for a growing country such as India. For a thriving agricultural and economic sector, plant disease identification is a crucial concern to enhance productivity requires attention. Conventional approaches for detecting plant diseases are gruelling, timeconsuming and demand a great deal of experience. Plant disease can be detected in a timely manner as it emerges on plant leaves, with the utilization of numerous machine learning and deep learning approaches. These high precision and time-efficient techniques are the way forward to generate qualitative farm produce. The current article examined a few methods currently used for analysing data sources, feature extraction, data augmentation and classification of plant diseases. PRISMA guidelines have been utilized to select the articles, using various keywords from peer-reviewed articles published in several databases between 2016 and 2025. Following the removal of studies based on the abstract, title, full text and conclusion, 75 publications were found and examined for their immediate relevance to plant disease recognition and categorization. Of these, 45 publications have been selected for this systematic review.