Recognition of Plant Leaf Diseases Using CNN

Abstract

Agriculture improvement is a global economic issue and ongoing challenge in this covid-19 pandemic that is highly dependent on effectiveness. The recognition of the diseases in plant leaf performs a major role in the agriculture industry and city-side greenhouse farms approximate analysis of the leaf disease this article intends to integrate image processing techniques with the “convolutional neural network”, which is one of the deep learning approaches, to classify and detect plant leaf disease and publicly available plant the late data to help treat the leaf as early as possible, which controls the economic loss. This paper has a set that was used which consists of 10 classes of disease and three classes of a plant leaf, this research offers an effective method for detecting different diseases in plant leaf variations. The model was created to detect and recognize healthy plant kinds, such as tomato and potato, and pepper these three leaves will perform under the algorithm called a convolutional neural network. By modifying the parameters and changing the pulling combination, models that have been used to train and test these types of leaf sample images can be created. leaf disease recognition was based on these 10 different types of classes in three different species tomato, potato, and pepper the classification of sample images has reached diseases identification accuracy.

Plant phenotyping relevance

植物葉の画像から病害状態をCNNで分類・検出する手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

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