A plant disease image using convolutional recurrent neural network procedure intended for big data plant classification

Abstract

The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%).

Plant phenotyping relevance

植物画像から病害状態を分類するCRNNベースの画像解析フレームワークを開発・比較しており、病害状態という植物表現型の抽出が中心である。

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