Plant Disease Detection Using Deep Learning

Abstract

Abstract: Early diagnosis of plant diseases is critical since they have a substantial impact on the growth of their unique species. Many Machine Learning (ML) models have been used to detect and categorize plant diseases, but recent breakthroughs in a subset of ML called Deep Learning (DL) look to hold a lot of promise in terms of improved accuracy. A variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments. In addition, a number of performance measurements are used to evaluate various architectures/techniques. This article explains how to use DL models to display a variety of plant diseases. Furthermore, several research gaps are identified, allowing for improved efficiency in detecting plant illnesses even before issues emerge. Keywords: Plant disease; deep learning; convolutional neural networks (CNN), Google Net Architecture, Tensorflow, and PyTorch are some of the tools that can be used;

Code and data availability

The paper uses the public PlantVillage leaf-image dataset as its phenotyping input, but no authors' public URL, code deposit, trained model release, or supplement is provided in the supplied blocks. The only URL present is the article's own DOI, which is not an actionable asset link, and no availability statement for作者

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