A Comprehensive Review of Research on Disease Prediction in Plant Leaves

Abstract

In agriculture, plant disease detection at an early stage is significant. Plant leaves are an important factor in plant disease detection. Early disease prevention in-line harvest loss benefit is given to plant growers. The odd leaves can be visible after getting infected, and if the software can tell accurately the disease infestation result. Forecasting plant leaf disease works better for an early disease warning. The infected leaf needs to be permanently removed, or it will affect the plant. As leaves are a significant part and produce food from the sun, the infected leaves show different patterns. Many proposing such AI and ML models have forecasted the infected leaves of many plants. This document focuses on forecasting diseases in plant leaves mainly for four types of plants: tomato, potato, apple & grape. Diseases in plant leaves can cause significant damage to plants and can have a detrimental effect on both the quantity and quality of production whether we consider a lower scale kitchen gardening or a high scale agriculture farming. The research begins with an overview of the common plant leaf diseases found in each of these four plants, along with their symptoms and causes. It then discusses the importance of early detection and management of these diseases to ensure healthy growth. This helps a naïve gardener as well as an experienced farmer to prevent a plant or a whole field from getting infected or diseased. Plant disease detection approaches are crucial for prevention and management. Various techniques in plant disease detection using AI- based machine learning and deep learning methods are reviewed comprehensively. Keywords— Agriculture farming, Kitchen Garden, Plant leaf diseases, Forecasting diseases.

Plant phenotyping relevance

植物葉の病徴・病害をAI/機械学習で検出・予測する手法を包括的にレビューしており、植物の病害状態を観測・推定するフェノタイピング手法が中心です。

Code and data availability

This is a literature review of plant leaf disease prediction with no authors' own phenotype datasets, images, code, or models released. The figures and datasets referenced (e.g., PlantVillage tomato/grape images, PlantDoc) are cited from prior third-party works, not paper-specific assets of this review.

No evidence-backed public reproduction asset is currently recorded.