Plant Leaf Disease Detection: Review Report

Abstract

Plant leaf disease detection is a crucial aspect of precision agriculture and crop management, helping to prevent crop losses and improves yield quality. Plants are very essential in our life they provide source of energy and overcome the matter of global warming. Plant disease is notable risk of nutrition security. Therefore, timely detection of risk is important. Leaf disease detection using the machine learning is an approach. Machine learning offers a worthy approach for making a classy and automatic algorithm using Convolutional Neural Network (CNN), Artificial Intelligence (AI), Image Processing and video processing, voice processing, Natural Language Processing, etc. This review report provides the comparative analysis of the different machine learning algorithms of diagnosis of different leaf disease.

Plant phenotyping relevance

植物葉の病徴・病害を画像と機械学習で検出する方法を比較分析するレビューであり、植物の状態推定に関する方法論が中心です。

Code and data availability

This is a literature review report on plant leaf disease detection with no original phenotyping measurements, datasets, images, code, or models of its own. Datasets like PlantVillage, PlantDoc, and FieldPlant are mentioned only as cited prior work, with no author-deposited assets or availability statements.

No evidence-backed public reproduction asset is currently recorded.