PLANT LEAF DISEASE IDENTIFICATION BASED ON MACHINE AND DEEP LEARNING

Abstract

Plant diseases significantly impact agriculture by reducing crop yield, causing financial losses, and increasing food insecurity.Therefore, accurate and early detection of plant leaf diseases is crucial to prevent disease spread and support effective treatment strategies.Traditional diagnosis based on visual inspection by experts is time-consuming, labor-intensive, and prone to human errors.Recent advancements in digital imaging and machine learning have enabled automated plant disease identification systems with higher accuracy and efficiency.However, both ML and DL models still face challenges such as limited datasets, image noise, and model generalization.This study analyzes existing ML and DL approaches, identifies their limitations, and highlights research gaps to guide the development of an improved framework for plant leaf disease detection.

Plant phenotyping relevance

植物葉の画像に基づく病害識別手法を分析するレビューであり、病害状態という植物表現型の取得・推定方法が中心です。

Code and data availability

The paper describes ML/DL plant leaf disease classification but provides no public dataset, image collection, code repository, model checkpoint, or supplement with availability language. Figures are illustrative screenshots/diagrams within the article itself, and no authors' public URL for assets is given.

No evidence-backed public reproduction asset is currently recorded.