Utilizing an Efficient Deep Convolutional Neural Network for the Automatic Classification of Plant Leaf Diseases

Abstract

Globally, India is second in terms of tomato and apple production. On an average of nearly, 18.399 tons of tomatoes and 30.500 tons of apples are produced in India. Likewise, after rice and wheat, maize scores third most significant food in India and it is one of the top crop producers in the world. The growth and health of these plants are typically afflicted by the diseases. Variety of leaf diseases available under apple, tomato and maize leaves which affect the production. This paper proposes an efficient deep convolutional neural network for identifying and detecting diseases in plant leaves through automatic image classification. The proposed model’s primary goal is to pinpoint a fix for the issue with leaf diseases that affect maize, tomatoes, and apples. The proposed efficient model comprises of residual layers, modified residual layers and global average pooling layers that yield better efficiency in terms of feature extraction and high throughput. The performance of the proposed model is evaluated through training and testing using the Plant Village dataset, which was sourced from a GitHub repository. In addition, the proposed model is evaluated through standard classification metrics and achieved better accuracy in the range 97% to 99%.

Plant phenotyping relevance

植物葉の病害状態を画像から自動分類する深層学習手法が研究の中心であり、病害表現型の取得・推定に該当する。

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