Deep learning-based plant classification and crop disease classification by thermal camera

被引:17
|
作者
Batchuluun, Ganbayar [1 ]
Nam, Se Hyun [1 ]
Park, Kang Ryoung [1 ]
机构
[1] Dongguk Univ, Div Elect & Elect Engn, 30 Pildong Ro 1 Gil, Seoul 04620, South Korea
基金
新加坡国家研究基金会;
关键词
Crop disease image; Plant image classification; Thermal image; Convolutional neural network; Explainable artificial intelligence;
D O I
10.1016/j.jksuci.2022.11.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Studies regarding image classification based on plant and crop disease images that were acquired using a visible light camera have been conducted in the past, whereas those based on thermal images are limited. This is because the thermal images are blurry due to the nature of the thermal camera, which makes it extremely difficult to classify objects. Therefore, this study proposes a new plant and crop disease classification method based on thermal images. The proposed method used a convolutional neural network with explainable artificial intelligence (XAI) to improve plant and crop disease classification performance. A new thermal plant image dataset was built for conducting the experiments, which contained 4,720 various images of flowers and leaves. In addition, an open database of crop diseases was also used, such as the Paddy crop dataset. The proposed plant and crop disease classification method demonstrated a 98.55% accuracy for the thermal plant image dataset and a 90.04% accuracy for the Paddy crop dataset, both of which outperformed other existing methods. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.
引用
收藏
页码:10474 / 10486
页数:13
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