Identification of Plant Diseases Using Imaging Processing and Machine Learning 1st

Abstract

This comprehensive review explores the most current developments in the use of image processing and machine learning methods for the diagnosis of plant diseases. After a thorough review of the literature, we critically assess the different frameworks and methods used to classify disorders affecting plants. Our study highlights the advantages and disadvantages of each approach while focusing on how accurate it is in diagnosing a wide variety of illnesses. In addition, we investigate new developments and trends in this quickly developing industry. We conclude by talking about the ongoing difficulties and suggesting some directions for further study to improve the effectiveness of plant disease detection systems. Keywords— CNN, VGG16, image processing, classification, neural networks, and machine learning

Plant phenotyping relevance

植物病害を画像処理・機械学習で診断する手法を中心に比較・批評するレビューであり、植物の病徴・病害状態を画像から推定するフェノタイピング手法レビューに該当する。

Code and data availability

The paper is a review-style study using the New Plant Diseases Dataset and CNN/VGG16/ensemble models, but no block contains an explicit data or code availability statement, deposit, or authors' public URL for the dataset, images, code, or trained models. No qualifying paper-specific public asset can be identified, and,

No evidence-backed public reproduction asset is currently recorded.