USING CONVOLUTIONAL NEURAL NETWORK FOR GRAPE PLANT DISEASE CLASSIFICATION

Abstract

Plant disease classification is the use of machine learning techniques for determining the type of disease from the input leaf images of the plants based on certain features. It is an important research area since early identification and treatment of plant disease is critical for saving crops, preventing agricultural disasters, and improving productivity in agriculture. This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors. It especially works on the classification of plant diseases for grape leaves from images by designing a deep-learning architecture. A web application was also implemented to help the agricultural workers. The experiments carried out on real-world images showed that a significant improvement (8.7%) on average was achieved by the proposed model (98.53%) against the state-of-the-art models (89.84%) in terms of accuracy.

Plant phenotyping relevance

ブドウ葉画像から病害状態を分類するCNNモデルの開発が中心であり、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 public PlantVillage grape leaf dataset, but that is cited prior work (Hughes and Salathe, 2015), not a paper-specific asset. No author code, trained model, web application source, or data deposit with a public URL is mentioned anywhere in the supplied blocks, and no allowed URLs are provided.

No evidence-backed public reproduction asset is currently recorded.