Detection and Classification of Plant Disease with Deep Learning

Abstract

Abstract: Deep learning is a branch of artificial intelligence. In recent years, with the benefits of automatic learning and feature extraction, it's been wide involved by educational and industrial circles. It has been wide utilized in image and video processing, voice processing, and natural language processing. At a similar time, it's conjointly become an enquiry hotspot within the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. the application of deep learning in disease recognition will avoid the disadvantages caused by artificial choice of illness spot options, make plant disease feature extraction additional objective, and improve the analysis potency and technology transformation speed. This paper provides the analysis progress of deep learning technology within the field of crop plant disease identification in recent years. during this paper, we tend to present this trends and challenges for the detection of plant leaf disease with deep learning and advanced imaging techniques. we tend to hope that this work are going to be a valuable resource for researchers UN agency study the detection of plant diseases and bug pests. At a similar time, we tend to conjointly mentioned some of the challenges and issues that require to be resolved. Keywords: Deep Learning, Disease Detection, Classification, Artificial Intelligence, Leaf Disease Detection;

Plant phenotyping relevance

植物葉の病害を画像・深層学習で検出・分類する方法の進展、課題、画像技術を扱うレビューであり、植物状態の表現型取得法が中心です。

Code and data availability

This is a review article on deep learning for plant disease detection with no original phenotyping measurements, datasets, images, code, or models of its own. All cited works are prior publications, and no availability statements or public URLs for paper-specific assets appear in the supplied blocks.

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