PLANT DISEASE RECOGNITION USING VGG-16

Abstract

Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

Plant phenotyping relevance

植物葉画像から病害状態を推定するVGG-16モデルの開発・評価が研究の中心であり、植物病害表現型の画像ベース推定に該当する。

Code and data availability

The paper uses the Plant Village dataset and a VGG-16 model, but provides no public URL, deposit, or availability statement for the authors' code, trained model, or paper-specific data. The Plant Village dataset is a cited external resource, not a paper-specific asset, and no allowed_urls are provided.

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