Plant Disease Identification Using Shallow Convolutional Neural Network

Abstract

Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

Plant phenotyping relevance

植物画像から病害状態を分類するCNN手法の開発・比較・検証が研究の中心であり、植物病害という表現型状態を直接推定しているため採用。

Code and data availability

The paper's corn disease classification experiments directly use the public Kaggle 'Corn or Maize Plant Leaf Diseases' dataset (4188 images), cited with an explicit public URL. The PlantVillage subset and authors' field images have no public deposit (data availability is on-request only), and no author analysis code or