Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM.

Abstract

Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious diseases which hinder its production. Surprisingly, so far no dataset is available for the diseases in this crop. The unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases. Pre-trained Visual Geometry Group 16 (VGG16) architecture has been used and the images have been converted to other color spaces namely Hue Saturation Value (HSV), YCbCr and grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 99.4%. The dataset also has been evaluated with other popular architectures and compared. In addition, VGG16 has been used as feature extractor from 8 th convolution layer and these features have been used for classifying diseases employing Multi-Class Support Vector Machine (MSVM). The analysis depicted an equivalent or in some cases produced better accuracy. Possible reasons for variation in interclass accuracy and future direction have been discussed.

Plant phenotyping relevance

ナスの病害状態を画像から分類するデータセットを構築し、VGG16・MSVM等を比較評価しており、植物病害フェノタイピング手法とデータセットが研究の中心である。

Code and data availability

The paper's eggplant disease image dataset (laboratory and field images of five diseases) is paper-specific and was created by the authors, but it is not publicly deposited; the Data availability statement says it is available only from the corresponding author on request. No public code, models, or other assets are披露.

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